Can You Trust Your Pose? Confidence Estimation in Visual Localization

Luca Ferranti, Xiaotian Li, Jani Boutellier, Juho Kannala

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Auto-TLDR; Pose Confidence Estimation in Large-Scale Environments: A Light-weight Approach to Improving Pose Estimation Pipeline

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Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations. The research so far has focused on improving subcomponents of estimation pipelines, to achieve more accurate poses. However, there is no guarantee for the result to be correct, even though the correctness of pose estimation is critically important in several visual localization applications, such as in autonomous navigation. In this paper we bring to attention a novel research question, pose confidence estimation, where we aim at quantifying how reliable the visually estimated pose is. We develop a novel confidence measure to fulfill this task and show that it can be flexibly applied to different datasets, indoor or outdoor, and for various visual localization pipelines. We also show that the proposed techniques can be used to accomplish a secondary goal: improving the accuracy of existing pose estimation pipelines. Finally, the proposed approach is computationally light-weight and adds only a negligible increase to the computational effort of pose estimation.

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RISEdb: A Novel Indoor Localization Dataset

Carlos Sanchez Belenguer, Erik Wolfart, Álvaro Casado Coscollá, Vitor Sequeira

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Auto-TLDR; Indoor Localization Using LiDAR SLAM and Smartphones: A Benchmarking Dataset

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In this paper we introduce a novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. Our acquisition pipeline is based on a portable LiDAR SLAM backpack to map the buildings and to accurately track the pose of the user as it moves freely inside them. We introduce the calibration procedures that enable us to acquire and geo-reference live data coming from different independent sensors rigidly attached to the backpack. This has allowed us to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.

Do We Really Need Scene-Specific Pose Encoders?

Yoli Shavit, Ron Ferens

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Auto-TLDR; Pose Regression Using Deep Convolutional Networks for Visual Similarity

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Visual pose regression models estimate the camera pose from a query image with a single forward pass. Current models learn pose encoding from an image using deep convolutional networks which are trained per scene. The resulting encoding is typically passed to a multi-layer perceptron in order to regress the pose. In this work, we propose that scene-specific pose encoders are not required for pose regression and that encodings trained for visual similarity can be used instead. In order to test our hypothesis, we take a shallow architecture of several fully connected layers and train it with pre-computed encodings from a generic image retrieval model. We find that these encodings are not only sufficient to regress the camera pose, but that, when provided to a branching fully connected architecture, a trained model can achieve competitive results and even surpass current state-of-the-art pose regressors in some cases. Moreover, we show that for outdoor localization, the proposed architecture is the only pose regressor, to date, consistently localizing in under 2 meters and 5 degrees.

Localization and Transformation Reconstruction of Image Regions: An Extended Congruent Triangles Approach

Afra'A Ahmad Alyosef, Christian Elias, Andreas Nürnberger

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Auto-TLDR; Outlier Filtering of Sub-Image Relations using Geometrical Information

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Most of the existing methods to localize (sub) image relations – a subclass of near-duplicate retrieval techniques – rely on the distinctiveness of matched features of the images being compared. These sets of matching features usually include a proportion of outliers, i.e. features linking non matching regions. In approaches that are designed for retrieval purposes only, these false matches usually have a minor impact on the final ranking. However, if also a localization of regions and corresponding image transformations should be computed, these false matches often have a more significant impact. In this paper, we propose a novel outlier filtering approach based on the geometrical information of the matched features. Our approach is similar to the RANSAC model, but instead of randomly selecting sets of matches and employ them to derive the homography transformation between images or image regions, we exploit in addition the geometrical relation of feature matches to find the best congruent triangle matches. Based on this information we classify outliers and determine the correlation between image regions. We compare our approach with state of art approaches using different feature models and various benchmark data sets (sub-image/panorama with affine transformation, adding blur, noise or scale change). The results indicate that our approach is more robust than the state of art approaches and is able to detect correlation even when most matches are outliers. Moreover, our approach reduces the pre-processing time to filter the matches significantly.

Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze

Dongfang Liu, Yiming Cui, Xiaolei Guo, Wei Ding, Baijian Yang, Yingjie Chen

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Auto-TLDR; Feature Voting for Robust Visual Localization in Urban Settings

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Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicles to use GPS to acquire location information. However, the application of GPS can result in severe challenges when vehicles run within the inner city where different kinds of structures may shadow the GPS signal and lead to inaccurate location results. To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization. Different from the conventional front-view-based method, our approach employs views from three directions (front, left, and right) and thus significantly improves the robustness of location prediction. In our work, we craft the proposed feature voting method into three state-of-the-art visual localization networks and modify their architectures properly so that they can be applied for vehicular operation. Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings. Our research sheds light on using the visual localization approach to help autonomous vehicles to find accurate location information in a city maze, within a desirable time constraint.

Leveraging a Weakly Adversarial Paradigm for Joint Learning of Disparity and Confidence Estimation

Matteo Poggi, Fabio Tosi, Filippo Aleotti, Stefano Mattoccia

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Auto-TLDR; Joint Training of Deep-Networks for Outlier Detection from Stereo Images

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Deep architectures represent the state-of-the-art for perceiving depth from stereo images. Although these methods are highly accurate, it is crucial to effectively detect any outlier through confidence measures since a wrong perception of even small portions of the sensed scene might lead to catastrophic consequences, for instance, in autonomous driving. Purposely, state-of-the-art confidence estimation methods rely on deep-networks as well. In this paper, arguing that these tasks are two sides of the same coin, we propose a novel paradigm for their joint training. Specifically, inspired by the successful deployment of GANs in other fields, we design two deep architectures: a generator for disparity estimation and a discriminator for distinguishing correct assignments from outliers. The two networks are jointly trained in a new peculiar weakly adversarial manner pushing the former to fix the errors detected by the discriminator while keeping the correct prediction unchanged. Experimental results on standard stereo datasets prove that such joint training paradigm yields significant improvements. Moreover, an additional outcome of our proposal is the ability to detect outliers with better accuracy compared to the state-of-the-art.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

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Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

Learning to Find Good Correspondences of Multiple Objects

Youye Xie, Yingheng Tang, Gongguo Tang, William Hoff

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Auto-TLDR; Multi-Object Inliers and Outliers for Perspective-n-Point and Object Recognition

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Given a set of 3D to 2D putative matches, labeling the correspondences as inliers or outliers plays a critical role in a wide range of computer vision applications including the Perspective-n-Point (PnP) and object recognition. In this paper, we study a more generalized problem which allows the matches to belong to multiple objects with distinct poses. We propose a deep architecture to simultaneously label the correspondences as inliers or outliers and classify the inliers into multiple objects. Specifically, we discretize the 3D rotation space into twenty convex cones based on the facets of a regular icosahedron. For each facet, a facet classifier is trained to predict the probability of a correspondence being an inlier for a pose whose rotation normal vector points towards this facet. An efficient RANSAC-based post-processing algorithm is also proposed to further process the prediction results and detect the objects. Experiments demonstrate that our method is very efficient compared to existing methods and is capable of simultaneously labeling and classifying the inliers of multiple objects with high precision.

ID Documents Matching and Localization with Multi-Hypothesis Constraints

Guillaume Chiron, Nabil Ghanmi, Ahmad Montaser Awal

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Auto-TLDR; Identity Document Localization in the Wild Using Multi-hypothesis Exploration

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This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, non-rigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.

Distinctive 3D Local Deep Descriptors

Fabio Poiesi, Davide Boscaini

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Auto-TLDR; DIPs: Local Deep Descriptors for Point Cloud Regression

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Benchmarking Cameras for OpenVSLAM Indoors

Kevin Chappellet, Guillaume Caron, Fumio Kanehiro, Ken Sakurada, Abderrahmane Kheddar

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Auto-TLDR; OpenVSLAM: Benchmarking Camera Types for Visual Simultaneous Localization and Mapping

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In this paper we benchmark different types of cameras and evaluate their performance in terms of reliable localization reliability and precision in Visual Simultaneous Localization and Mapping (vSLAM). Such benchmarking is merely found for visual odometry, but never for vSLAM. Existing studies usually compare several algorithms for a given camera. %This work is the first to handle the dual of the latter, i.e. comparing several cameras for a given SLAM algorithm. The evaluation methodology we propose is applied to the recent OpenVSLAM framework. The latter is versatile enough to natively deal with perspective, fisheye, 360 cameras in a monocular or stereoscopic setup, an in RGB or RGB-D modalities. Results in various sequences containing light variation and scenery modifications in the scene assess quantitatively the maximum localization rate for 360 vision. In the contrary, RGB-D vision shows the lowest localization rate, but highest precision when localization is possible. Stereo-fisheye trades-off with localization rates and precision between 360 vision and RGB-D vision. The dataset with ground truth will be made available in open access to allow evaluating other/future vSLAM algorithms with respect to these camera types.

Extending Single Beam Lidar to Full Resolution by Fusing with Single Image Depth Estimation

Yawen Lu, Yuxing Wang, Devarth Parikh, Guoyu Lu

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Auto-TLDR; Self-supervised LIDAR for Low-Cost Depth Estimation

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Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution Lidars are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a learning-based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 50-100 times cheaper price (e.g., \$80000 Velodyne HDL-64E LIDAR v.s. \$1000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.

Learning to Segment Dynamic Objects Using SLAM Outliers

Dupont Romain, Mohamed Tamaazousti, Hervé Le Borgne

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Auto-TLDR; Automatic Segmentation of Dynamic Objects Using SLAM Outliers Using Consensus Inversion

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We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network. We integrate the trained network in ORB-SLAM 2 and LDSO. At runtime we remove features on dynamic objects, making the SLAM unaffected by them. We also propose a new stereo dataset and new metrics to evaluate SLAM robustness. Our dataset includes consensus inversions, i.e., situations where the SLAM uses more features on dynamic objects that on the static background. Consensus inversions are challenging for SLAM as they may cause major SLAM failures. Our approach performs better than the State-of-the-Art on the TUM RGB-D dataset in monocular mode and on our dataset in both monocular and stereo modes.

Occlusion-Tolerant and Personalized 3D Human Pose Estimation in RGB Images

Ammar Qammaz, Antonis Argyros

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Auto-TLDR; Real-Time 3D Human Pose Estimation in BVH using Inverse Kinematics Solver and Neural Networks

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We introduce a real-time method that estimates the 3D human pose directly in the popular BVH format, given estimations of the 2D body joints in RGB images. Our contributions include: (a) A novel and compact 2D pose representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the H3.6M dataset compared to the baseline MocapNET method while maintaining real-time performance (70 fps in CPU-only execution).

AV-SLAM: Autonomous Vehicle SLAM with Gravity Direction Initialization

Kaan Yilmaz, Baris Suslu, Sohini Roychowdhury, L. Srikar Muppirisetty

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Auto-TLDR; VI-SLAM with AGI: A combination of three SLAM algorithms for autonomous vehicles

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Simultaneous localization and mapping (SLAM) algorithms that are aimed at autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2\%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs.

Unconstrained Vision Guided UAV Based Safe Helicopter Landing

Arindam Sikdar, Abhimanyu Sahu, Debajit Sen, Rohit Mahajan, Ananda Chowdhury

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Auto-TLDR; Autonomous Helicopter Landing in Hazardous Environments from Unmanned Aerial Images Using Constrained Graph Clustering

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In this paper, we have addressed the problem of automated detection of safe zone(s) for helicopter landing in hazardous environments from images captured by an Unmanned Aerial Vehicle (UAV). The unconstrained motion of the image capturing drone (the UAV in our case) makes the problem further difficult. The solution pipeline consists of natural landmark detection and tracking, stereo-pair generation using constrained graph clustering, digital terrain map construction and safe landing zone detection. The main methodological contribution lies in mathematically formulating epipolar constraint and then using it in a Minimum Spanning Tree (MST) based graph clustering approach. We have also made publicly available AHL (Autonomous Helicopter Landing) dataset, a new aerial video dataset captured by a drone, with annotated ground-truths. Experimental comparisons with other competing clustering methods i) in terms of Dunn Index and Davies Bouldin Index as well as ii) for frame-level safe zone detection in terms of F-measure and confusion matrix clearly demonstrate the effectiveness of the proposed formulation.

Recovery of 2D and 3D Layout Information through an Advanced Image Stitching Algorithm Using Scanning Electron Microscope Images

Aayush Singla, Bernhard Lippmann, Helmut Graeb

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Auto-TLDR; Image Stitching for True Geometrical Layout Recovery in Nanoscale Dimension

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Image stitching describes the process of reconstruction of a high resolution image from combining multiple images. Using a scanning electron microscope as the image source, individual images will show patterns in a nm dimension whereas the combined image may cover an area of several mm2. The recovery of the physical layout of modern semiconductor products manufactured in advanced technologies nodes down to 22 nm requires a perfect stitching process with no deviation with respect to the original design data, as any stitching error will result in failures during the reconstruction of the electrical design. In addition, the recovery of the complete design requires the acquisition of all individual layers of a semiconductor device which represent a 3D structure with interconnections defining error limits on the stitching error for each individual scanned image mosaic. An advanced stitching and alignment process is presented enabling a true geometrical layout recovery in nanoscale dimensions which is also applied and evaluated on other use cases from biological applications.

Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings

Daniele De Gregorio, Riccardo Zanella, Gianluca Palli, Luigi Di Stefano

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Auto-TLDR; Automated Deep Learning for Robotic Grasping Applications

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In this paper we investigate how to effectively deploy deep learning in practical industrial settings, such as robotic grasping applications. When a deep-learning based solution is proposed, usually lacks of any simple method to generate the training data. In the industrial field, where automation is the main goal, not bridging this gap is one of the main reasons why deep learning is not as widespread as it is in the academic world. For this reason, in this work we developed a system composed by a 3-DoF Pose Estimator based on Convolutional Neural Networks (CNNs) and an effective procedure to gather massive amounts of training images in the field with minimal human intervention. By automating the labeling stage, we also obtain very robust systems suitable for production-level usage. An open source implementation of our solution is provided, alongside with the dataset used for the experimental evaluation.

Orthographic Projection Linear Regression for Single Image 3D Human Pose Estimation

Yahui Zhang, Shaodi You, Theo Gevers

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Auto-TLDR; A Deep Neural Network for 3D Human Pose Estimation from a Single 2D Image in the Wild

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3D human pose estimation from a single 2D image in the wild is an important computer vision task but yet extremely challenging. Unlike images taken from indoor and well constrained environments, 2D outdoor images in the wild are extremely complex because of varying imaging conditions. Furthermore, 2D images usually do not have corresponding 3D pose ground truth making a supervised approach ill constrained. Therefore, in this paper, we propose to associate the 3D human pose, the 2D human pose projection and the 2D image appearance through a new orthographic projection based linear regression module. Unlike existing reprojection based approaches, our orthographic projection and regression do not suffer from small angle problems, which usually lead to overfitting in the depth dimension. Hence, we propose a deep neural network which adopts the 2D pose, 3D pose regression and orthographic projection linear regression module. The proposed method shows state-of-the art performance on the Human3.6M dataset and generalizes well to in-the-wild images.

Generic Merging of Structure from Motion Maps with a Low Memory Footprint

Gabrielle Flood, David Gillsjö, Patrik Persson, Anders Heyden, Kalle Åström

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Auto-TLDR; A Low-Memory Footprint Representation for Robust Map Merge

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With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data — from both a hand held mobile phone and from a drone — we verify the performance of the proposed method.

Total Estimation from RGB Video: On-Line Camera Self-Calibration, Non-Rigid Shape and Motion

Antonio Agudo

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Auto-TLDR; Joint Auto-Calibration, Pose and 3D Reconstruction of a Non-rigid Object from an uncalibrated RGB Image Sequence

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In this paper we present a sequential approach to jointly retrieve camera auto-calibration, camera pose and the 3D reconstruction of a non-rigid object from an uncalibrated RGB image sequence, without assuming any prior information about the shape structure, nor the need for a calibration pattern, nor the use of training data at all. To this end, we propose a Bayesian filtering approach based on a sum-of-Gaussians filter composed of a bank of extended Kalman filters (EKF). For every EKF, we make use of dynamic models to estimate its state vector, which later will be Gaussianly combined to achieve a global solution. To deal with deformable objects, we incorporate a mechanical model solved by using the finite element method. Thanks to these ingredients, the resulting method is both efficient and robust to several artifacts such as missing and noisy observations as well as sudden camera motions, while being available for a wide variety of objects and materials, including isometric and elastic shape deformations. Experimental validation is proposed in real experiments, showing its strengths with respect to competing approaches.

RefiNet: 3D Human Pose Refinement with Depth Maps

Andrea D'Eusanio, Stefano Pini, Guido Borghi, Roberto Vezzani, Rita Cucchiara

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Auto-TLDR; RefiNet: A Multi-stage Framework for 3D Human Pose Estimation

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Human Pose Estimation is a fundamental task for many applications in the Computer Vision community and it has been widely investigated in the 2D domain, i.e. intensity images. Therefore, most of the available methods for this task are mainly based on 2D Convolutional Neural Networks and huge manually-annotated RGB datasets, achieving stunning results. In this paper, we propose RefiNet, a multi-stage framework that regresses an extremely-precise 3D human pose estimation from a given 2D pose and a depth map. The framework consists of three different modules, each one specialized in a particular refinement and data representation, i.e. depth patches, 3D skeleton and point clouds. Moreover, we collect a new dataset, namely Baracca, acquired with RGB, depth and thermal cameras and specifically created for the automotive context. Experimental results confirm the quality of the refinement procedure that largely improves the human pose estimations of off-the-shelf 2D methods.

Minimal Solvers for Indoor UAV Positioning

Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck, Kalle Åström, Anders Heyden

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Auto-TLDR; Relative Pose Solvers for Visual Indoor UAV Navigation

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In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor UAV navigation. We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a variety of realistic indoor scenarios, which makes it possible to navigate using images of the ground floor. Current state-of-the-art solvers use more general assumptions, such as using arbitrary planar structures; however, these solvers do not yield adequate reconstructions for real scenes, nor do they perform fast enough to be incorporated in real-time systems. We show that the proposed solvers enjoy better numerical stability, are faster, and require fewer point correspondences, compared to state-of-the-art solvers. These properties are vital components for robust navigation in real-time systems, and we demonstrate on both synthetic and real data that our method outperforms other methods, and yields superior motion estimation.

Multi-Scale Keypoint Matching

Sina Lotfian, Hassan Foroosh

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Auto-TLDR; Multi-Scale Keypoint Matching Using Multi-Scale Information

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We propose a new hierarchical method to match keypoints by exploiting information across multiple scales. Traditionally, for each keypoint a single scale is detected and the matching process is done in the specific scale. We replace this approach with matching across scale-space. The holistic information from higher scales are used for early rejection of candidates that are far away in the feature space. The more localized and finer details of lower scale are then used to decide between remaining possible points. The proposed multi-scale solution is more consistent with the multi-scale processing that is present in the human visual system and is therefore biologically plausible. We evaluate our method on several datasets and achieve state of the art accuracy, while significantly outperforming others in extraction time.

Calibration and Absolute Pose Estimation of Trinocular Linear Camera Array for Smart City Applications

Martin Ahrnbom, Mikael Nilsson, Håkan Ardö, Kalle Åström, Oksana Yastremska-Kravchenko, Aliaksei Laureshyn

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Auto-TLDR; Trinocular Linear Camera Array Calibration for Traffic Surveillance Applications

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A method for calibrating a Trinocular Linear Camera Array (TLCA) for traffic surveillance applications, such as towards smart cities, is presented. A TLCA-specific parametrization guarantees that the calibration finds a model where all the cameras are on a straight line. The method uses both a chequerboard close to the camera, as well as measured 3D points far from the camera: points measured in world coordinates, as well as their corresponding 2D points found manually in the images. Superior calibration accuracy can be obtained compared to standard methods using only a single data source, largely due to the use of chequerboards, while the line constraint in the parametrization allows for joint rectification. The improved triangulation accuracy, from 8-12 cm to around 6 cm when calibrating with 30-50 points in our experiment, allowing better road user analysis. The method is demonstrated by a proof-of-concept application where a point cloud is generated from multiple disparity maps, visualizing road user detections in 3D.

Object-Oriented Map Exploration and Construction Based on Auxiliary Task Aided DRL

Junzhe Xu, Jianhua Zhang, Shengyong Chen, Honghai Liu

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Auto-TLDR; Auxiliary Task Aided Deep Reinforcement Learning for Environment Exploration by Autonomous Robots

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Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or multiple fixed goals, while ignoring the perception and construction of external environments. In this paper, we propose a novel environment exploration task based on DRL, which requires a robot fast and completely perceives all objects of interest, and reconstructs their poses in a global environment map, as much as the robot can do. To this end, we design an auxiliary task aided DRL model, which is integrated with the auxiliary object detection and 6-DoF pose estimation components. The outcome of auxiliary tasks can improve the learning speed and robustness of DRL, as well as the accuracy of object pose estimation. Comprehensive experimental results on the indoor simulation platform AI2-THOR have shown the effectiveness and robustness of our method.

Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

Bing Zha, Alper Yilmaz

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Auto-TLDR; Exploiting Motion Trajectories for Geolocalization of Object on Topological Map using Recurrent Neural Network

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In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent geolocalization of object. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization. We pose the learning process as a conditional sequence prediction problem in which each output locates the object on a traversable edge in a map. Considering the prediction sequence ought to be topologically connected in the graph-structured map, we adopt two different hypotheses generation and elimination strategies to eliminate disconnected sequence prediction. We demonstrate our approach on the KITTI stereo visual odometry dataset which is a city-scale environment. The key benefits of our approach to geolocalization are that 1) we take advantage of powerful sequence modeling ability of recurrent neural network and its robustness to noisy input, 2) only require a map in the form of a graph and 3) simply use an affordable sensor that generates motion trajectory. The experiments show that the motion trajectories can be learned by training an recurrent neural network, and temporally consistent geolocation can be predicted with both of the proposed strategies.

Story Comparison for Estimating Field of View Overlap in a Video Collection

Thierry Malon, Sylvie Chambon, Alain Crouzil, Vincent Charvillat

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Auto-TLDR; Finding Videos with Overlapping Fields of View Using Video Data

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Determining the links between large amounts of video data with no prior knowledge of the camera positions is a hard task to automate. From a collection of videos acquired from static cameras simultaneously, we propose a method for finding groups of videos with overlapping fields of view. Each video is first processed individually: at regular time steps, objects are detected and are assigned a category and an appearance descriptor. Next, the video is split into cells at different resolutions and we assign to each cell its story: it consists of the list of objects detected in the cell over time. Once the stories are established for each video, the links between cells of different videos are determined by comparing their stories: two cells are linked if they show simultaneous detections of objects of the same category with similar appearances. Pairs of videos with overlapping fields of view are identified using these links between cells. A link graph is finally returned, in which each node represents a video, and the edges indicate pairs of overlapping videos. The approach is evaluated on a set of 63 real videos from both public datasets and live surveillance videos, as well as on 84 synthetic videos, and shows promising results.

Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Ya Lu, Thomai Stathopoulou, Stavroula Mougiakakou

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Auto-TLDR; Food Volume Estimation from a Single Food Image via Geometric Understanding and Semantic Prediction

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Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.

A Plane-Based Approach for Indoor Point Clouds Registration

Ketty Favre, Muriel Pressigout, Luce Morin, Eric Marchand

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Auto-TLDR; A plane-based registration approach for indoor environments based on LiDAR data

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Iterative Closest Point (ICP) is one of the mostly used algorithms for 3D point clouds registration. This classical approach can be impacted by the large number of points contained in a point cloud. Planar structures, which are less numerous than points, can be used in well-structured man-made environment. In this paper we propose a registration method inspired by the ICP algorithm in a plane-based registration approach for indoor environments. This method is based solely on data acquired with a LiDAR sensor. A new metric based on plane characteristics is introduced to find the best plane correspondences. The optimal transformation is estimated through a two-step minimization approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is more robust in large motion scenarios than other state-of-the-art algorithms.

On Embodied Visual Navigation in Real Environments through Habitat

Marco Rosano, Antonino Furnari, Luigi Gulino, Giovanni Maria Farinella

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Auto-TLDR; Learning Navigation Policies on Real World Observations using Real World Images and Sensor and Actuation Noise

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Visual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations through reinforcement learning. Unfortunately, collecting the required experience deploying a robotic platform in the real world is expensive and time-consuming. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. Despite the advantages they offer, simulators present a limited realism in terms of appearance and physical dynamics, leading to navigation policies that do not generalize in the real world. In this paper, we propose a tool based on the Habitat simulator which exploits real world images of the environment, together with sensor and actuator noise models, to produce more realistic navigation episodes. We perform a range of experiments using virtual, real and images transformed with a simple domain adaptation approach. We also assess the impact of sensor and actuation noise on the navigation performance and investigate whether they allow to learn more robust navigation policies. We show that our tool can effectively help to train and evaluate navigation policies on real world observations without running navigation episodes in the real world.

A Two-Step Approach to Lidar-Camera Calibration

Yingna Su, Yaqing Ding, Jian Yang, Hui Kong

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Auto-TLDR; Closed-Form Calibration of Lidar-camera System for Ego-motion Estimation and Scene Understanding

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Autonomous vehicles and robots are typically equipped with Lidar and camera. Hence, calibrating the Lidar-camera system is of extreme importance for ego-motion estimation and scene understanding. In this paper, we propose a two-step approach (coarse + fine) for the external calibration between a camera and a multiple-line Lidar. First, a new closed-form solution is proposed to obtain the initial calibration parameters. We compare our solution with the state-of-the-art SVD-based algorithm, and show the benefits of both the efficiency and stability. With the initial calibration parameters, the ICP-based calibration framework is used to register the point clouds which extracted from the camera and Lidar coordinate frames, respectively. Our method has been applied to two Lidar-camera systems: an HDL-64E Lidar-camera system, and a VLP-16 Lidar-camera system. Experimental results demonstrate that our method achieves promising performance and higher accuracy than two open-source methods.

PA-FlowNet: Pose-Auxiliary Optical Flow Network for Spacecraft Relative Pose Estimation

Zhi Yu Chen, Po-Heng Chen, Kuan-Wen Chen, Chen-Yu Chan

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Auto-TLDR; PA-FlowNet: An End-to-End Pose-auxiliary Optical Flow Network for Space Travel and Landing

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During the process of space travelling and space landing, the spacecraft attitude estimation is the indispensable work for navigation. Since there are not enough satellites for GPS-like localization in space, the computer vision technique is adopted to address the issue. The most crucial task for localization is the extraction of correspondences. In computer vision, optical flow estimation is often used for finding correspondences between images. As the deep neural network being more popular in recent years, FlowNet2 has played a vital role which achieves great success. In this paper, we present PA-FlowNet, an end-to-end pose-auxiliary optical flow network which can use the predicted relative camera pose to improve the performance of optical flow. PA-FlowNet is composed of two sub-networks, the foreground-attention flow network and the pose regression network. The foreground-attention flow network is constructed bybased on FlowNet2 model and modified with the proposed foreground-attention approach. We introduced this approach with the concept of curriculum learning for foreground-background segmentation to avoid backgrounds from resulting in flow prediction error. The pose regression network is used to regress the relative camera pose as an auxiliary for increasing the accuracy of the flow estimation. In addition, to simulate the test environment for spacecraft pose estimation, we construct a 64K moon model and to simulate aerial photography with various attitudes to generate Moon64K dataset in this paper. PA-FlowNet significantly outperforms all existing methods on our the proposed Moon64K dataset. Furthermore, we also predict the relative pose via proposed PA-FlowNet and accomplish the remarkable performance.

Motion Segmentation with Pairwise Matches and Unknown Number of Motions

Federica Arrigoni, Tomas Pajdla, Luca Magri

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Auto-TLDR; Motion Segmentation using Multi-Modelfitting andpermutation synchronization

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In this paper we address motion segmentation, that is the problem of clustering points in multiple images according to a number of moving objects. Two-frame correspondences are assumed as input without prior knowledge about trajectories. Our method is based on principles from ''multi-model fitting'' and ''permutation synchronization'', and - differently from previous techniques working under the same assumptions - it can handle an unknown number of motions. The proposed approach is validated on standard datasets, showing that it can correctly estimate the number of motions while maintaining comparable or better accuracy than the state of the art.

Deep Next-Best-View Planner for Cross-Season Visual Route Classification

Kurauchi Kanya, Kanji Tanaka

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Auto-TLDR; Active Visual Place Recognition using Deep Convolutional Neural Network

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This paper addresses the problem of active visual place recognition (VPR) from a novel perspective of long-term autonomy. In our approach, a next-best-view (NBV) planner plans an optimal action-observation-sequence to maximize the expected cost-performance for a visual route classification task. A difficulty arises from the fact that the NBV planner is trained and tested in different domains (times of day, weather conditions, and seasons). Existing NBV methods may be confused and deteriorated by the domain-shifts, and require significant efforts for adapting them to a new domain. We address this issue by a novel deep convolutional neural network (DNN) -based NBV planner that does not require the adaptation. Our main contributions in this paper are summarized as follows: (1) We present a novel domain-invariant NBV planner that is specifically tailored for DNN-based VPR. (2) We formulate the active VPR as a POMDP problem and present a feasible solution to address the inherent intractability. Specifically, the probability distribution vector (PDV) output by the available DNN is used as a domain-invariant observation model without the need to retrain it. (3) We verify efficacy of the proposed approach through challenging cross-season VPR experiments, where it is confirmed that the proposed approach clearly outperforms the previous single-view-based or multi-view-based VPR in terms of VPR accuracy and/or action-observation-cost.

Loop-closure detection by LiDAR scan re-identification

Jukka Peltomäki, Xingyang Ni, Jussi Puura, Joni-Kristian Kamarainen, Heikki Juhani Huttunen

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Auto-TLDR; Loop-Closing Detection from LiDAR Scans Using Convolutional Neural Networks

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In this work, loop-closure detection from LiDAR scans is defined as an image re-identification problem. Re-identification is performed by computing Euclidean distances of a query scan to a gallery set of previous scans. The distances are computed in a feature embedding space where the scans are mapped by a convolutional neural network (CNN). The network is trained using the triplet loss training strategy. In our experiments we compare different backbone networks, variants of the triplet loss and generic and LiDAR specific data augmentation techniques. With a realistic indoor dataset the best architecture obtains the mean average precision (mAP) above 90%.

RONELD: Robust Neural Network Output Enhancement for Active Lane Detection

Zhe Ming Chng, Joseph Mun Hung Lew, Jimmy Addison Lee

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Auto-TLDR; Real-Time Robust Neural Network Output Enhancement for Active Lane Detection

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Accurate lane detection is critical for navigation in autonomous vehicles, particularly the active lane which demarcates the single road space that the vehicle is currently traveling on. Recent state-of-the-art lane detection algorithms utilize convolutional neural networks (CNNs) to train deep learning models on popular benchmarks such as TuSimple and CULane. While each of these models works particularly well on train and test inputs obtained from the same dataset, the performance drops significantly on unseen datasets of different environments. In this paper, we present a real-time robust neural network output enhancement for active lane detection (RONELD) method to identify, track, and optimize active lanes from deep learning probability map outputs. We first adaptively extract lane points from the probability map outputs, followed by detecting curved and straight lanes before using weighted least squares linear regression on straight lanes to fix broken lane edges resulting from fragmentation of edge maps in real images. Lastly, we hypothesize true active lanes through tracking preceding frames. Experimental results demonstrate an up to two-fold increase in accuracy using RONELD on cross-dataset validation tests.

On Identification and Retrieval of Near-Duplicate Biological Images: A New Dataset and Protocol

Thomas E. Koker, Sai Spandana Chintapalli, San Wang, Blake A. Talbot, Daniel Wainstock, Marcelo Cicconet, Mary C. Walsh

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Auto-TLDR; BINDER: Bio-Image Near-Duplicate Examples Repository for Image Identification and Retrieval

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Manipulation and re-use of images in scientific publications is a growing issue, not only for biomedical publishers, but also for the research community in general. In this work we introduce BINDER -- Bio-Image Near-Duplicate Examples Repository, a novel dataset to help researchers develop, train, and test models to detect same-source biomedical images. BINDER contains 7,490 unique image patches for model training, 1,821 same-size patch duplicates for validation and testing, and 868 different-size image/patch pairs for image retrieval validation and testing. Except for the training set, patches already contain manipulations including rotation, translation, scale, perspective transform, contrast adjustment and/or compression artifacts. We further use the dataset to demonstrate how novel adaptations of existing image retrieval and metric learning models can be applied to achieve high-accuracy inference results, creating a baseline for future work. In aggregate, we thus present a supervised protocol for near-duplicate image identification and retrieval without any "real-world" training example. Our dataset and source code are available at hms-idac.github.io/BINDER.

Novel View Synthesis from a 6-DoF Pose by Two-Stage Networks

Xiang Guo, Bo Li, Yuchao Dai, Tongxin Zhang, Hui Deng

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Auto-TLDR; Novel View Synthesis from a 6-DoF Pose Using Generative Adversarial Network

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Novel view synthesis is a challenging problem in 3D vision and robotics. Different from the existing works, which need the reference images or 3D model, we propose a novel paradigm to this problem. That is, we synthesize the novel view from a 6-DoF pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model which could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. The GenNet generates a coarse image from a camera pose. The RefineNet is a generative adversarial network that could refine the coarse image. In this way, we decouple the geometric relationship mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis. We will share our code after the acceptance of this work.

Approach for Document Detection by Contours and Contrasts

Daniil Tropin, Sergey Ilyuhin, Dmitry Nikolaev, Vladimir V. Arlazarov

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Auto-TLDR; A countor-based method for arbitrary document detection on a mobile device

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This paper considers the task of arbitrary document detection performed on a mobile device. The classical contour-based approach often mishandles cases with occlusion, complex background, or blur. Region-based approach, which relies on the contrast between object and background, does not have limitations, however its known implementations are highly resource-consuming. We propose a modification of a countor-based method, in which the competing hypotheses of the contour location are ranked according to the contrast between the areas inside and outside the border. In the performed experiments such modification leads to the 40% decrease of alternatives ordering errors and 10% decrease of the overall number of detection errors. We updated state-of-the-art performance on the open MIDV-500 dataset and demonstrated competitive results with the state-of-the-art on the SmartDoc dataset.

Street-Map Based Validation of Semantic Segmentation in Autonomous Driving

Laura Von Rueden, Tim Wirtz, Fabian Hueger, Jan David Schneider, Nico Piatkowski, Christian Bauckhage

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Auto-TLDR; Semantic Segmentation Mask Validation Using A-priori Knowledge from Street Maps

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Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness, which motivates the thorough validation of learned models. However, current validation approaches mostly require ground truth data and are thus both cost-intensive and limited in their applicability. We propose to overcome these limitations by a model agnostic validation using a-priori knowledge from street maps. In particular, we show how to validate semantic segmentation masks and demonstrate the potential of our approach using OpenStreetMap. We introduce validation metrics that indicate false positive or negative road segments. Besides the validation approach, we present a method to correct the vehicle's GPS position so that a more accurate localization can be used for the street map based validation. Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.

Computing Stable Resultant-Based Minimal Solvers by Hiding a Variable

Snehal Bhayani, Zuzana Kukelova, Janne Heikkilä

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Auto-TLDR; Sparse Permian-Based Method for Solving Minimal Systems of Polynomial Equations

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Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e., solving minimal problems, in a RANSAC-style framework. Minimal problems often result in complex systems of polynomial equations. The existing state-of-the-art methods for solving such systems are either based on Groebner Basis and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a resultant computation using an extra variable. In this paper, we study an interesting alternative resultant-based method for solving sparse systems of polynomial equations by hiding one variable. This approach results in a larger eigenvalue problem than the action matrix and extra variable resultant-based methods; however, it does not need to compute an inverse or elimination of large matrices that may be numerically unstable. The proposed approach includes several improvements to the standard sparse resultant algorithms, which significantly improves the efficiency and stability of the hidden variable resultant-based solvers as we demonstrate on several interesting computer vision problems. We show that for the studied problems, our sparse resultant based approach leads to more stable solvers than the state-of-the-art Groebner Basis as well as existing resultant-based solvers, especially in close to critical configurations. Our new method can be fully automated and incorporated into existing tools for the automatic generation of efficient minimal solvers.

Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-To-Video Search

Savas Ozkan, Gözde Bozdağı Akar

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Auto-TLDR; Fast and Robust Image-to-Video Retrieval Using Local and Global Descriptors

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Cost-effective visual representation and fast query-by-example search are two challenging goals hat should be provided for web-scale visual retrieval task on a moderate hardware. In this paper, we introduce a fast yet robust method that ensures both of these goals by obtaining the state-of-the-art results for an image-to-video search scenario. To this end, we present important enhancements to commonly used indexing and visual representation techniques by promoting faster, better and more moderate retrieval performance. We also boost the effectiveness of the method for visual distortion by exploiting the individual decision results of local and global descriptors in the query time. By this way, local content descriptors effectively represent copied / duplicated scenes with large geometric deformations, while global descriptors for near duplicate and semantic searches are more practical. Experiments are conducted on the large-scale Stanford I2V dataset. The experimental results show that the method is effective in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. In addition, the proposed method is fairly accurate and achieves state-of-the-art performance based on the mAP score of the dataset. Lastly, we report additional mAP scores after updating the ground annotations obtained by the retrieval results of the proposed method showing more clearly the actual performance.

Adaptive Estimation of Optimal Color Transformations for Deep Convolutional Network Based Homography Estimation

Miguel A. Molina-Cabello, Jorge García-González, Rafael Marcos Luque-Baena, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio

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Auto-TLDR; Improving Homography Estimation from a Pair of Natural Images Using Deep Convolutional Neural Networks

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Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.

Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry

Oussema Bouafif, Bogdan Khomutenko, Mohammed Daoudi

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Auto-TLDR; Recovering 3D Head Geometry from a Single Image using Deep Learning and Geometric Techniques

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Recovering the 3D geometric structure of a face from a single input image is a challenging active research area in computer vision. In this paper, we present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only. It predicts both pixel-wise normal vectors and landmarks maps from a single input photo. Landmarks are used for the pose computation and the initialization of the optimization problem, which, in turn, reconstructs the 3D head geometry by using a parametric morphable model and normal vector fields. State-of-the-art results are achieved through qualitative and quantitative evaluation tests on both single and multi-view settings. Despite the fact that the model was trained only on synthetic data, it successfully recovers 3D geometry and precise poses for real-world images.

Multi-View Object Detection Using Epipolar Constraints within Cluttered X-Ray Security Imagery

Brian Kostadinov Shalon Isaac-Medina, Chris G. Willcocks, Toby Breckon

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Auto-TLDR; Exploiting Epipolar Constraints for Multi-View Object Detection in X-ray Security Images

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Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object detection labels is proposed. In addition, detections are given a score based on its similarity with respect to the distribution of the error of the epipolar estimation. This score is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

HP2IFS: Head Pose Estimation Exploiting Partitioned Iterated Function Systems

Carmen Bisogni, Michele Nappi, Chiara Pero, Stefano Ricciardi

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Auto-TLDR; PIFS based head pose estimation using fractal coding theory and Partitioned Iterated Function Systems

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Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches.

Light3DPose: Real-Time Multi-Person 3D Pose Estimation from Multiple Views

Alessio Elmi, Davide Mazzini, Pietro Tortella

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Auto-TLDR; 3D Pose Estimation of Multiple People from a Few calibrated Camera Views using Deep Learning

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We present an approach to perform 3D pose estimation of multiple people from a few calibrated camera views. Our architecture, leveraging the recently proposed unprojection layer, aggregates feature-maps from a 2D pose estimator backbone into a comprehensive representation of the 3D scene. Such intermediate representation is then elaborated by a fully-convolutional volumetric network and a decoding stage to extract 3D skeletons with sub-voxel accuracy. Our method achieves state of the art MPJPE on the CMU Panoptic dataset using a few unseen views and obtains competitive results even with a single input view. We also assess the transfer learning capabilities of the model by testing it against the publicly available Shelf dataset obtaining good performance metrics. The proposed method is inherently efficient: as a pure bottom-up approach, it is computationally independent of the number of people in the scene. Furthermore, even though the computational burden of the 2D part scales linearly with the number of input views, the overall architecture is able to exploit a very lightweight 2D backbone which is orders of magnitude faster than the volumetric counterpart, resulting in fast inference time. The system can run at 6 FPS, processing up to 10 camera views on a single 1080Ti GPU.

Tackling Occlusion in Siamese Tracking with Structured Dropouts

Deepak Gupta, Efstratios Gavves, Arnold Smeulders

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Auto-TLDR; Structured Dropout for Occlusion in latent space

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Occlusion is one of the most difficult challenges in object tracking to model. This is because unlike other challenges, where data augmentation can be of help, occlusion is hard to simulate as the occluding object can be anything in any shape. In this paper, we propose a simple solution to simulate the effects of occlusion in the latent space. Specifically, we present structured dropout to mimic the change in latent codes under occlusion. We present three forms of dropout (channel dropout, segment dropout and slice dropout) with the various forms of occlusion in mind. To demonstrate its effectiveness, the dropouts are incorporated into two modern Siamese trackers (SiamFC and SiamRPN++). The outputs from multiple dropouts are combined using an encoder network to obtain the final prediction. Experiments on several tracking benchmarks show the benefits of structured dropouts, while due to their simplicity requiring only small changes to the existing tracker models.