Human Segmentation with Dynamic LiDAR Data

Tao Zhong, Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi

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Auto-TLDR; Spatiotemporal Neural Network for Human Segmentation with Dynamic Point Clouds

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Consecutive LiDAR scans and depth images compose dynamic 3D sequences, which contain more abundant spatiotemporal information than a single frame. Similar to the development history of image and video perception, dynamic 3D sequence perception starts to come into sight after inspiring research on static 3D data perception. This work proposes a spatiotemporal neural network for human segmentation with the dynamic LiDAR point clouds. It takes a sequence of depth images as input. It has a two-branch structure, i.e., the spatial segmentation branch and the temporal velocity estimation branch. The velocity estimation branch is designed to capture motion cues from the input sequence and then propagates them to the other branch. So that the segmentation branch segments humans according to both spatial and temporal features. These two branches are jointly learned on a generated dynamic point cloud data set for human recognition. Our works fill in the blank of dynamic point cloud perception with the spherical representation of point cloud and achieves high accuracy. The experiments indicate that the introduction of temporal feature benefits the segmentation of dynamic point cloud perception.

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PC-Net: A Deep Network for 3D Point Clouds Analysis

Zhuo Chen, Tao Guan, Yawei Luo, Yuesong Wang

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Auto-TLDR; PC-Net: A Hierarchical Neural Network for 3D Point Clouds Analysis

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Due to the irregularity and sparsity of 3D point clouds, applying convolutional neural networks directly on them can be nontrivial. In this work, we propose a simple but effective approach for 3D Point Clouds analysis, named PC-Net. PC-Net directly learns on point sets and is equipped with three new operations: first, we apply a novel scale-aware neighbor search for adaptive neighborhood extracting; second, for each neighboring point, we learn a local spatial feature as a complement to their associated features; finally, at the end we use a distance re-weighted pooling to aggregate all the features from local structure. With this module, we design hierarchical neural network for point cloud understanding. For both classification and segmentation tasks, our architecture proves effective in the experiments and our models demonstrate state-of-the-art performance over existing deep learning methods on popular point cloud benchmarks.

Video Semantic Segmentation Using Deep Multi-View Representation Learning

Akrem Sellami, Salvatore Tabbone

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Auto-TLDR; Deep Multi-view Representation Learning for Video Object Segmentation

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In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., $F_{a}$ and $F_{b}$. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

Ghost Target Detection in 3D Radar Data Using Point Cloud Based Deep Neural Network

Mahdi Chamseddine, Jason Rambach, Oliver Wasenmüler, Didier Stricker

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Auto-TLDR; Point Based Deep Learning for Ghost Target Detection in 3D Radar Point Clouds

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Ghost targets are targets that appear at wrong locations in radar data and are caused by the presence of multiple indirect reflections between the target and the sensor. In this work, we introduce the first point based deep learning approach for ghost target detection in 3D radar point clouds. This is done by extending the PointNet network architecture by modifying its input to include radar point features beyond location and introducing skip connetions. We compare different input modalities and analyze the effects of the changes we introduced. We also propose an approach for automatic labeling of ghost targets 3D radar data using lidar as reference. The algorithm is trained and tested on real data in various driving scenarios and the tests show promising results in classifying real and ghost radar targets.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

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Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

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Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

Semantic Segmentation for Pedestrian Detection from Motion in Temporal Domain

Guo Cheng, Jiang Yu Zheng

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Auto-TLDR; Motion Profile: Recognizing Pedestrians along with their Motion Directions in a Temporal Way

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In autonomous driving, state-of-the-art methods detect pedestrian through appearance in 2-D spatial images. However, these approaches are typically time-consuming because of the complexity of algorithms to cope with large variations in shape, pose, action, and illumination. They also fall short of capturing temporal continuity in motion trace. In a completely different approach, this work recognizes pedestrians along with their motion directions in a temporal way. By projecting a driving video to a 2-D temporal image called Motion Profile (MP), we can robustly distinguish pedestrian in motion and standing-still against smooth background motion. To ensure non-redundant data processing of deep network on a compact motion profile further, a novel temporal-shift memory (TSM) model is developed to perform deep learning of sequential input in linear processing time. In experiments containing various pedestrian motion from sensors such as video and LiDAR, we demonstrate that, with the data size around 3/720th of video volume, this motion-based method can reach the detecting rate of pedestrians at 90% in near and mid-range on the road. With a super-fast processing speed and good accuracy, this method is promising for intelligent vehicles.

Sensor-Independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

Takahiro Shimizu, Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno, Motoki Shino

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Auto-TLDR; CosPointPillars: A 3D Object Detection Method for Pedestrian Detection in Walking Spaces

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Autonomous driving of a personal mobility vehicle such as a wheelchair in a walking space is necessary in the future as a means of transportation for the elderly and the physically handicapped. To realize this, accurate pedestrian detection is indispensable. As existing 3D object detection methods are trained with a roadway dataset, they are widely used for object detection in roadways. These methods have two major issues in the detection of objects in walking spaces. The first issue is that they are largely affected by the difference of the LIDAR models. To eliminate this issue, we propose a 3D object detection method, CosPointPillars. CosPointPillars does not take the reflection intensities of LIDAR point cloud, which cause a sensor model dependency, as input. Furthermore, CosPointPillars utilizes a cosine estimation network (CEN) to retain the detection accuracy. The second issue is that networks trained with a roadway dataset cannot sufficiently detect pedestrians (who are major traffic participants in walking spaces) located within a short distance; this is because the roadway dataset hardly includes nearby pedestrians. To solve this issue, we generated a new walking space dataset called SimDataset, which includes nearby pedestrians as a training dataset in the simulations. An experiment on the KITTI showed that the CEN helps in pedestrian detection in sparse point clouds. Furthermore, an experiment on a real walking space showed that SimDataset is suitable for pedestrian detection in such cases.

Progressive Scene Segmentation Based on Self-Attention Mechanism

Yunyi Pan, Yuan Gan, Kun Liu, Yan Zhang

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Auto-TLDR; Two-Stage Semantic Scene Segmentation with Self-Attention

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Semantic scene segmentation is vital for a large variety of applications as it enables understanding of 3D data. Nowadays, various approaches based upon point clouds ignore the mathematical distribution of points and treat the points equally. The methods following this direction neglect the imbalance problem of samples that naturally exists in scenes. To avoid these issues, we propose a two-stage semantic scene segmentation framework based on self-attention mechanism and achieved state-of-the-art performance on 3D scene understanding tasks. We split the whole task into two small ones which efficiently relief the sample imbalance issue. In addition, we have designed a new self-attention block which could be inserted into submanifold convolution networks to model the long-range dependencies that exists among points. The proposed network consists of an encoder and a decoder, with the spatial-wise and channel-wise attention modules inserted. The two-stage network shares a U-Net architecture and is an end-to-end trainable framework which could predict the semantic label for the scene point clouds fed into it. Experiments on standard benchmarks of 3D scenes implies that our network could perform at par or better than the existing state-of-the-art methods.

Object Segmentation Tracking from Generic Video Cues

Amirhossein Kardoost, Sabine Müller, Joachim Weickert, Margret Keuper

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Auto-TLDR; A Light-Weight Variational Framework for Video Object Segmentation in Videos

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We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning. Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS 16,17 and SegTrack v2.

Residual Learning of Video Frame Interpolation Using Convolutional LSTM

Keito Suzuki, Masaaki Ikehara

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Auto-TLDR; Video Frame Interpolation Using Residual Learning and Convolutional LSTMs

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Video frame interpolation aims to generate interme- diate frames between the original frames. This produces videos with a higher frame r ate and creates smoother motion. Many video frame interpolation methods first estimate the motion vector between the input frames and then synthesizes the intermediate frame based on the motion. However, these methods rely on the accuracy of the motion estimation step and fail to accurately generate the interpolated frame when the estimated motion vectors are inaccurate. Therefore, to avoid the uncertainties caused by motion estimation, this paper proposes a method that directly generates the intermediate frame. Since two consecutive frames are relatively similar, our method takes the average of these two frames and utilizes residual learning to learn the difference between the average of these frames and the ground truth middle frame. In addition, our method uses Convolutional LSTMs and four input frames to better incorporate spatiotemporal information. This neural network can be easily trained end to end without difficult to obtain data such as optical flow. Our experimental results show that the proposed method can perform favorably against other state-of-the-art frame interpolation methods.

NetCalib: A Novel Approach for LiDAR-Camera Auto-Calibration Based on Deep Learning

Shan Wu, Amnir Hadachi, Damien Vivet, Yadu Prabhakar

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Auto-TLDR; Automatic Calibration of LiDAR and Cameras using Deep Neural Network

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A fusion of LiDAR and cameras have been widely used in many robotics applications such as classification, segmentation, object detection, and autonomous driving. It is essential that the LiDAR sensor can measure distances accurately, which is a good complement to the cameras. Hence, calibrating sensors before deployment is a mandatory step. The conventional methods include checkerboards, specific patterns, or human labeling, which is trivial and human-labor extensive if we do the same calibration process every time. The main propose of this research work is to build a deep neural network that is capable of automatically finding the geometric transformation between LiDAR and cameras. The results show that our model manages to find the transformations from randomly sampled artificial errors. Besides, our work is open-sourced for the community to fully utilize the advances of the methodology for developing more the approach, initiating collaboration, and innovation in the topic.

ACCLVOS: Atrous Convolution with Spatial-Temporal ConvLSTM for Video Object Segmentation

Muzhou Xu, Shan Zong, Chunping Liu, Shengrong Gong, Zhaohui Wang, Yu Xia

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Auto-TLDR; Semi-supervised Video Object Segmentation using U-shape Convolution and ConvLSTM

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Semi-supervised video object segmentation aims at segmenting the target of interest throughout a video sequence when only the annotated mask of the first frame is given. A feasible method for segmentation is to capture the spatial-temporal coherence between frames. However, it may suffer from mask drift when the spatial-temporal coherence is unreliable. To relieve this problem, we propose an encoder-decoder-recurrent model for semi-supervised video object segmentation. The model adopts a U-shape architecture that combines atrous convolution and ConvLSTM to establish the coherence in both the spatial and temporal domains. Furthermore, the weight ratio for each block is also reconstructed to make the model more suitable for the VOS task. We evaluate our method on two benchmarks, DAVIS-2017 and Youtube-VOS, where state-of-the-art segmentation accuracy with a real-time inference speed of 21.3 frames per second on a Tesla P100 is obtained.

MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth Features

Hangtao Feng, Lu Zhang, Xu Yang, Zhiyong Liu

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Auto-TLDR; MixedFusion: Combining Color and Point Clouds for 6D Pose Estimation

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Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, Due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, show that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.

Cross-Regional Attention Network for Point Cloud Completion

Hang Wu, Yubin Miao

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Auto-TLDR; Learning-based Point Cloud Repair with Graph Convolution

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Point clouds obtained from real word scanning are always incomplete and ununiformly distributed, which would cause structural losses in 3D shape representations. Therefore, a learning-based method is introduced in this paper to repair partial point clouds and restore the complete shapes of target objects. First, we design an encoder that takes both local features and global features into consideration. Second, we establish a graph to connect the local features together, and then implement graph convolution with multi-head attention on it. The graph enables each local feature vector to search across the regions and selectively absorb other local features based on the its own features and global features. Third, we design a coarse decoder to collect cross-region features from the graph and generate coarse point clouds with low resolution, and a folding-based decoder to generate fine point clouds with high resolution. Our network is trained on six categories of objects in the ModelNet dataset, and its performance is compared with several existing methods, the results show that our network is able to generate dense complete point cloud with the highest accuracy.

PointDrop: Improving Object Detection from Sparse Point Clouds Via Adversarial Data Augmentation

Wenxin Ma, Jian Chen, Qing Du, Wei Jia

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Auto-TLDR; PointDrop: Improving Robust 3D Object Detection to Sparse Point Clouds

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Current 3D object detection methods achieve accurate and efficient results on the standard point cloud dataset. However, in real-world applications, due to the expensive cost of obtaining the annotated 3D object detection data, we expect to directly apply the model trained on the standard dataset to real-world scenarios. This strategy may fail because the point cloud samples obtained in the real-world scenarios may be much sparser due to various reasons (occlusion, low reflectivity of objects and fewer laser beams) and existing methods do not consider the limitations of their models on sparse point clouds. To improve the robustness of an object detector to sparser point clouds, we propose PointDrop, which learns to drop the features of some key points in the point clouds to generate challenging sparse samples for data augmentation. Moreover, PointDrop is able to adjust the difficulty of the generated samples based on the capacity of the detector and thus progressively improve the performance of the detector. We create two sparse point clouds datasets from the KITTI dataset to evaluate our method, and the experimental results show that PointDrop significantly improves the robustness of the detector to sparse point clouds.

MANet: Multimodal Attention Network Based Point-View Fusion for 3D Shape Recognition

Yaxin Zhao, Jichao Jiao, Ning Li

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Auto-TLDR; Fusion Network for 3D Shape Recognition based on Multimodal Attention Mechanism

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3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

Revisiting Sequence-To-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory

Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio, Federico Raue, Jörn Hees, Andreas Dengel

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Auto-TLDR; Sequence-to-Sequence Learning for Video Object Segmentation

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Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentation mask for the first frame, the task is to provide pixel-accurate masks for the object over the rest of the sequence. Despite much progress in the last years, we noticed that many of the existing approaches lose objects in longer sequences, especially when the object is small or briefly occluded. In this work, we build upon a sequence-to-sequence approach that employs an encoder-decoder architecture together with a memory module for exploiting the sequential data. We further improve this approach by proposing a model that manipulates multi-scale spatio-temporal information using memory-equipped skip connections. Furthermore, we incorporate an auxiliary task based on distance classification which greatly enhances the quality of edges in segmentation masks. We compare our approach to the state of the art and show considerable improvement in the contour accuracy metric and the overall segmentation accuracy.

Motion U-Net: Multi-Cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon, Filiz Bunyak, Kannappan Palaniappan

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Auto-TLDR; Motion U-Net: A Deep Learning Framework for Robust Moving Object Detection under Challenging Conditions

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Detection of moving objects is a critical first step in many computer vision applications. Several algorithms for motion and change detection were proposed. However, many of these approaches lack the ability to handle challenging real-world scenarios. Recently, deep learning approaches started to produce impressive solutions to computer vision tasks, particularly for detection and segmentation. Many existing deep learning networks proposed for moving object detection rely only on spatial appearance cues. In this paper, we propose a novel multi-cue and multi-stream network, Motion U-Net (MU-Net), which integrates motion, change, and appearance cues using a deep learning framework for robust moving object detection under challenging conditions. The proposed network consists of a two-stream encoder module followed by feature concatenation and a decoder module. Motion and change cues are computed through our tensor-based motion estimation and a multi-modal background subtraction modules. The proposed system was tested and evaluated on the change detection challenge datasets (CDnet-2014) and compared to state-of-the-art methods. On CDnet-2014 dataset, our approach reaches an average overall F-measure of 0.9852 and outperforms all current state-of-the-art methods. The network was also tested on the unseen SBI-2015 dataset and produced promising results.

Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model

Congcong Li, Haoyu Ma, Qingmin Liao

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Auto-TLDR; Adaptive object scene flow estimation using a hybrid CNN-CRF model and adaptive iteration

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Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.

PointSpherical: Deep Shape Context for Point Cloud Learning in Spherical Coordinates

Hua Lin, Bin Fan, Yongcheng Liu, Yirong Yang, Zheng Pan, Jianbo Shi, Chunhong Pan, Huiwen Xie

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Auto-TLDR; Spherical Hierarchical Modeling of 3D Point Cloud

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We propose Spherical Hierarchical modeling of 3D point cloud. Inspired by Shape Context, we design a receptive field on each 3D point by placing a spherical coordinate on it. We sample points using the furthest point method and creating overlapping balls of points. For each ball, we divide the space into radial, polar angular and azimuthal angular bins on which we form a Spherical Hierarchy. We apply 1x1 CNN convolution on points to start the initial feature extraction. Repeated 3D CNN and max pooling over the Spherical bins propagate contextual information until all the information is condensed in the center bin. Extensive experiments on five datasets strongly evidence that our method outperform current models on various Point Cloud Learning tasks, including 2D/3D shape classification, 3D part segmentation and 3D semantic segmentation.

Learning Object Deformation and Motion Adaption for Semi-Supervised Video Object Segmentation

Xiaoyang Zheng, Xin Tan, Jianming Guo, Lizhuang Ma

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Auto-TLDR; Semi-supervised Video Object Segmentation with Mask-propagation-based Model

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We propose a novel method to solve the task of semi-supervised video object segmentation in this paper, where the mask annotation is only given at the first frame of the video sequence. A mask-propagation-based model is applied to learn the past and current information for segmentation. Besides, due to the scarcity of training data, image/mask pairs that model object deformation and shape variance are generated for the training phase. In addition, we generate the key flips between two adjacent frames for motion adaptation. The method works in an end-to-end way, without any online fine-tuning on test videos. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art algorithms on benchmark datasets, covering cases with single object or multiple objects. We also conduct extensive ablation experiments to analyze the effectiveness of our proposed method.

FatNet: A Feature-Attentive Network for 3D Point Cloud Processing

Chaitanya Kaul, Nick Pears, Suresh Manandhar

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Auto-TLDR; Feature-Attentive Neural Networks for Point Cloud Classification and Segmentation

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The application of deep learning to 3D point clouds is challenging due to its lack of order. Inspired by the point embeddings of PointNet and the edge embeddings of DGCNNs, we propose three improvements to the task of point cloud analysis. First, we introduce a novel feature-attentive neural network layer, a FAT layer, that combines both global point-based features and local edge-based features in order to generate better embeddings. Second, we find that applying the same attention mechanism across two different forms of feature map aggregation, max pooling and average pooling, gives better performance than either alone. Third, we observe that residual feature reuse in this setting propagates information more effectively between the layers, and makes the network easier to train. Our architecture achieves state-of-the-art results on the task of point cloud classification, as demonstrated on the ModelNet40 dataset, and an extremely competitive performance on the ShapeNet part segmentation challenge.

Holistic Grid Fusion Based Stop Line Estimation

Runsheng Xu, Faezeh Tafazzoli, Li Zhang, Timo Rehfeld, Gunther Krehl, Arunava Seal

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Auto-TLDR; Fused Multi-Sensory Data for Stop Lines Detection in Intersection Scenarios

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Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the vehicle. Most of the existing methods in literature solely use cameras to detect stop lines, which is typically not sufficient in terms of detection range. To address this issue, we propose a method that takes advantage of fused multi-sensory data including stereo camera and lidar as input and utilizes a carefully designed convolutional neural network architecture to detect stop lines. Our experiments show that the proposed approach can improve detection range compared to camera data alone, works under heavy occlusion without observing the ground markings explicitly, is able to predict stop lines for all lanes and allows detection at a distance up to 50 meters.

Real-Time Monocular Depth Estimation with Extremely Light-Weight Neural Network

Mian Jhong Chiu, Wei-Chen Chiu, Hua-Tsung Chen, Jen-Hui Chuang

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Auto-TLDR; Real-Time Light-Weight Depth Prediction for Obstacle Avoidance and Environment Sensing with Deep Learning-based CNN

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Obstacle avoidance and environment sensing are crucial applications in autonomous driving and robotics. Among all types of sensors, RGB camera is widely used in these applications as it can offer rich visual contents with relatively low-cost, and using a single image to perform depth estimation has become one of the main focuses in resent research works. However, prior works usually rely on highly complicated computation and power-consuming GPU to achieve such task; therefore, we focus on developing a real-time light-weight system for depth prediction in this paper. Based on the well-known encoder-decoder architecture, we propose a supervised learning-based CNN with detachable decoders that produce depth predictions with different scales. We also formulate a novel log-depth loss function that computes the difference of predicted depth map and ground truth depth map in log space, so as to increase the prediction accuracy for nearby locations. To train our model efficiently, we generate depth map and semantic segmentation with complex teacher models. Via a series of ablation studies and experiments, it is validated that our model can efficiently performs real-time depth prediction with only 0.32M parameters, with the best trained model outperforms previous works on KITTI dataset for various evaluation matrices.

Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

Dexiang Hong, Guorong Li, Kai Xu, Li Su, Qingming Huang

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Auto-TLDR; Siamese Dynamic Mask Estimation for Video Object Segmentation

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Video object segmentation(VOS) has been a fundamental topic in recent years, and many deep learning-based methods have achieved state-of-the-art performance on multiple benchmarks. However, most of these methods rely on pixel-level matching between the template and the searched frames on the whole image while the targets only occupy a small region. Calculating on the entire image brings lots of additional computation cost. Besides, the whole image may contain some distracting information resulting in many false-positive matching points. To address this issue, motivated by one-stage instance object segmentation methods, we propose an efficient siamese dynamic mask estimation network for fast video object segmentation. The VOS is decoupled into two tasks, i.e. mask feature learning and dynamic kernel prediction. The former is responsible for learning high-quality features to preserve structural geometric information, and the latter learns a dynamic kernel which is used to convolve with the mask feature to generate a mask output. We use Siamese neural network as a feature extractor and directly predict masks after correlation. In this way, we can avoid using pixel-level matching, making our framework more simple and efficient. Experiment results on DAVIS 2016 /2017 datasets show that our proposed methods can run at 35 frames per second on NVIDIA RTX TITAN while preserving competitive accuracy.

CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations

Arthur Ouaknine, Alasdair Newson, Julien Rebut, Florence Tupin, Patrick Pérez

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Auto-TLDR; CARRADA: A dataset of synchronized camera and radar recordings with range-angle-Doppler annotations for autonomous driving

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High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset will be released.

PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation

Na Zhao, Tat Seng Chua, Gim Hee Lee

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Auto-TLDR; PS2-Net: A Local and Globally Aware Deep Learning Framework for Semantic Segmentation on 3D Point Clouds

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In this paper, we present the PS^2-Net - a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds. In order to deeply incorporate local structures and global context to support 3D scene segmentation, our network is built on four repeatedly stacked encoders, where each encoder has two basic components: EdgeConv that captures local structures and NetVLAD that models global context. Different from existing start-of-the-art methods for point-based scene semantic segmentation that either violate or do not achieve permutation invariance, our PS2-Net is designed to be permutation invariant which is an essential property of any deep network used to process unordered point clouds. We further provide theoretical proof to guarantee the permutation invariance property of our network. We perform extensive experiments on two large-scale 3D indoor scene datasets and demonstrate that our PS2-Net is able to achieve state-of-the-art performances as compared to existing approaches.

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.

PHNet: Parasite-Host Network for Video Crowd Counting

Shiqiao Meng, Jiajie Li, Weiwei Guo, Jinfeng Jiang, Lai Ye

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Auto-TLDR; PHNet: A Parasite-Host Network for Video Crowd Counting

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Crowd counting plays an increasingly important role in public security. Recently, many crowd counting methods for a single image have been proposed but few studies have focused on using temporal information from image sequences of videos to improve prediction performance. In the existing methods using videos for crowd estimation, temporal features and spatial features are modeled jointly for the prediction, which makes the model less efficient in extracting spatiotemporal features and difficult to improve the performance of predictions. In order to solve these problems, this paper proposes a Parasite-Host Network(PHNet) which is composed of Parasite branch and Host branch to extract temporal features and spatial features respectively. To specifically extract the transform features in the time domain, we propose a novel architecture termed as “Relational Extractor”(RE) which models the multiplicative interaction features of adjacent frames. In addition, the Host branch extracts the spatial features from a current frame which can be replaced with any model that uses a single image for the prediction. We conducted experiments by using our PHNet on four video crowd counting benchmarks: Venice,UCSD,FDST and CrowdFlow. Experimental results show that PHnet achieves superior performance on these four datasets to the state-of-the-art methods.

Self-Supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data

Nikolas Müller, Jonas Stenzel, Jian-Jia Chen

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Auto-TLDR; A self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data

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Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and less publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. The solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional feedforward network and the computation of the pose for each detected object instance.

Attention Based Coupled Framework for Road and Pothole Segmentation

Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray

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Auto-TLDR; Few Shot Learning for Road and Pothole Segmentation on KITTI and IDD

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In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Manual-Label Free 3D Detection Via an Open-Source Simulator

Zhen Yang, Chi Zhang, Zhaoxiang Zhang, Huiming Guo

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Auto-TLDR; DA-VoxelNet: A Novel Domain Adaptive VoxelNet for LIDAR-based 3D Object Detection

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LiDAR based 3D object detectors typically need a large amount of detailed-labeled point cloud data for training, but these detailed labels are commonly expensive to acquire. In this paper, we propose a manual-label free 3D detection algorithm that leverages the CARLA simulator to generate a large amount of self-labeled training samples and introduces a novel Domain Adaptive VoxelNet (DA-VoxelNet) that can cross the distribution gap from the synthetic data to the real scenario. The self-labeled training samples are generated by a set of high quality 3D models embedded in a CARLA simulator and a proposed LiDAR-guided sampling algorithm. Then a DA-VoxelNet that integrates both a sample-level DA module and an anchor-level DA module is proposed to enable the detector trained by the synthetic data to adapt to real scenario. Experimental results show that the proposed unsupervised DA 3D detector on KITTI evaluation set can achieve 76.66% and 56.64% mAP on BEV mode and 3D mode respectively. The results reveal a promising perspective of training a LIDAR-based 3D detector without any hand-tagged label.

3D Semantic Labeling of Photogrammetry Meshes Based on Active Learning

Mengqi Rong, Shuhan Shen, Zhanyi Hu

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Auto-TLDR; 3D Semantic Expression of Urban Scenes Based on Active Learning

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As different urban scenes are similar but still not completely consistent, coupled with the complexity of labeling directly in 3D, high-level understanding of 3D scenes has always been a tricky problem. In this paper, we propose a procedural approach for 3D semantic expression of urban scenes based on active learning. We first start with a small labeled image set to fine-tune a semantic segmentation network and then project its probability map onto a 3D mesh model for fusion, finally outputs a 3D semantic mesh model in which each facet has a semantic label and a heat model showing each facet’s confidence. Our key observation is that our algorithm is iterative, in each iteration, we use the output semantic model as a supervision to select several valuable images for annotation to co-participate in the fine-tuning for overall improvement. In this way, we reduce the workload of labeling but not the quality of 3D semantic model. Using urban areas from two different cities, we show the potential of our method and demonstrate its effectiveness.

Deep Space Probing for Point Cloud Analysis

Yirong Yang, Bin Fan, Yongcheng Liu, Hua Lin, Jiyong Zhang, Xin Liu, 蔡鑫宇 蔡鑫宇, Shiming Xiang, Chunhong Pan

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Auto-TLDR; SPCNN: Space Probing Convolutional Neural Network for Point Cloud Analysis

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3D points distribute in a continuous 3D space irregularly, thus directly adapting 2D image convolution to 3D points is not an easy job. Previous works often artificially divide the space into regular grids, yet it could be suboptimal to learn geometry. In this paper, we propose SPCNN, namely, Space Probing Convolutional Neural Network, which naturally generalizes image CNN to deal with point clouds. The key idea of SPCNN is learning to probe the 3D space in an adaptive manner. Specifically, we define a pool of learnable convolutional weights, and let each point in the local region learn to choose a suitable convolutional weight from the pool. This is achieved by constructing a geometry guided index-mapping function that implicitly establishes a correspondence between convolutional weights and some local regions in the neighborhood (Fig. 1). In this way, the index-mapping function learns to adaptively partition nearby space for local geometry pattern recognition. With this convolution as a basic operator, SPCNN, a hierarchical architecture can be developed for effective point cloud analysis. Extensive experiments on challenging benchmarks across three tasks demonstrate that SPCNN achieves the state-of-the-art or has competitive performance.

A Lightweight Network to Learn Optical Flow from Event Data

Zhuoyan Li, Jiawei Shen

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Auto-TLDR; A lightweight pyramid network with attention mechanism to learn optical flow from events data

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Existing deep neural networks have found success in estimation of event-based optical flow, but are at the expense of complicated architectures. Moreover, few prior works discuss how to tackle with the noise problem of event camera, which would severely contaminate the data quality and make estimation an ill-posed problem. In this work, we present a lightweight pyramid network with attention mechanism to learn optical flow from events data. Specially, the network is designed according to two-well established principles: Laplacian pyramidal decomposition and channel attention mechanism. By integrating Laplacian pyramidal processing into CNN, the learning problem is simplified into several subproblems at each pyramid level, which can be handled by a relatively shallow network with few parameters. The channel attention block, embedded in each pyramid level, treats channels of feature map unequally and provides extra flexibility in suppressing background noises. The size of the proposed network is about only 5% of previous methods while our method still achieves state-of-the-art performance on the benchmark dataset. The experimental video samples of continuous flow estimation is presented at :https://github.com/xfleezy/blob.

MFI: Multi-Range Feature Interchange for Video Action Recognition

Sikai Bai, Qi Wang, Xuelong Li

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Auto-TLDR; Multi-range Feature Interchange Network for Action Recognition in Videos

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Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

Towards Efficient 3D Point Cloud Scene Completion Via Novel Depth View Synthesis

Haiyan Wang, Liang Yang, Xuejian Rong, Ying-Li Tian

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Auto-TLDR; 3D Point Cloud Completion with Depth View Synthesis and Depth View synthesis

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3D point cloud completion has been a long-standing challenge at scale, and corresponding per-point supervised training strategies suffered from the cumbersome annotations. 2D supervision has recently emerged as a promising alternative for 3D tasks, but specific approaches for 3D point cloud completion still remain to be explored. To overcome these limitations, we propose an end-to-end method that directly lifts a single depth map to a completed point cloud. With one depth map as input, a multi-way novel depth view synthesis network (NDVNet) is designed to infer coarsely completed depth maps under various viewpoints. Meanwhile, a geometric depth perspective rendering module is introduced to utilize the raw input depth map to generate a re-projected depth map for each view. Therefore, the two parallelly generated depth maps for each view are further concatenated and refined by a depth completion network (DCNet). The final completed point cloud is fused from all refined depth views. Experimental results demonstrate the effectiveness of our proposed approach composed of aforementioned components, to produce high-quality state-of-the-art results on the popular SUNCG benchmark.

Temporal Pulses Driven Spiking Neural Network for Time and Power Efficient Object Recognition in Autonomous Driving

Wei Wang, Shibo Zhou, Jingxi Li, Xiaohua Li, Junsong Yuan, Zhanpeng Jin

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Auto-TLDR; Spiking Neural Network for Real-Time Object Recognition on Temporal LiDAR Pulses

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Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been widely applied in this area, their considerable processing latency, power consumption as well as computational complexity have been challenging issues for real-time autonomous driving applications. In this paper, we propose an approach to address the real-time object recognition problem utilizing spiking neural networks (SNNs). The proposed SNN model works directly with raw temporal LiDAR pulses without the pulse-to-point cloud preprocessing procedure, which can significantly reduce delay and power consumption. Being evaluated on various datasets derived from LiDAR and dynamic vision sensor (DVS), including Sim LiDAR, KITTI, and DVS-barrel, our proposed model has shown remarkable time and power efficiency, while achieving comparable recognition performance as the state-of-the-art methods. This paper highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to directly perform time and energy efficient object recognition on temporal LiDAR pulses in the setting of autonomous driving.

Yolo+FPN: 2D and 3D Fused Object Detection with an RGB-D Camera

Ya Wang

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Auto-TLDR; Yolo+FPN: Combining 2D and 3D Object Detection for Real-Time Object Detection

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In this paper we propose a new deep neural network system, called Yolo+FPN, which fuses both 2D and 3D object detection algorithms to achieve better real-time object detection results and faster inference speed, to be used on real robots. Finding an optimized fusion strategy to efficiently combine 3D object detection with 2D detection information is useful and challenging for both indoor and outdoor robots. In order to satisfy real-time requirements, a trade-off between accuracy and efficiency is needed. We not only have improved training and test accuracies and lower mean losses on the KITTI object detection benchmark, but also achieve better average precision on 3D detection of all classes in three levels of difficulty. Also, we implemented Yolo+FPN system using an RGB-D camera, and compared the speed of 2D and 3D object detection using different GPUs. For the real implementation of both indoor and outdoor scenes, we focus on person detection, which is the most challenging and important among the three classes.

Incorporating Depth Information into Few-Shot Semantic Segmentation

Yifei Zhang, Desire Sidibe, Olivier Morel, Fabrice Meriaudeau

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Auto-TLDR; RDNet: A Deep Neural Network for Few-shot Segmentation Using Depth Information

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Few-shot segmentation presents a significant challenge for semantic scene understanding under limited supervision. Namely, this task targets at generalizing the segmentation ability of the model to new categories given a few samples. In order to obtain complete scene information, we extend the RGB-centric methods to take advantage of complementary depth information. In this paper, we propose a two-stream deep neural network based on metric learning. Our method, known as RDNet, learns class-specific prototype representations within RGB and depth embedding spaces, respectively. The learned prototypes provide effective semantic guidance on the corresponding RGB and depth query image, leading to more accurate performance. Moreover, we build a novel outdoor scene dataset, known as Cityscapes-3i, using labeled RGB images and depth images from the Cityscapes dataset. We also perform ablation studies to explore the effective use of depth information in few-shot segmentation tasks. Experiments on Cityscapes-3i show that our method achieves promising results with visual and complementary geometric cues from only a few labeled examples.

S-VoteNet: Deep Hough Voting with Spherical Proposal for 3D Object Detection

Yanxian Chen, Huimin Ma, Xi Li, Xiong Luo

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Auto-TLDR; S-VoteNet: 3D Object Detection with Spherical Bounded Box Prediction

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Current 3D object detection methods adopt an analogous box prediction structure with the 2D methods, which predict center and size of the object simultaneously in a box regression procedure, leading to the poor performance of 3D detector to a great extent. In this work, we propose S-VoteNet, which converts the prediction of 3D bounding box into two parts: center prediction and size prediction. By introducing a novel spherical proposal, S-VoteNet uses vote groups to predict the center and radius of object rather than all parameters of 3D bounding box. The prediction of radius is used to constrain the object size, and the radius-based spherical center loss is applied to measure the geometric distance between the proposal and ground-truth. To make better use of the geometric information provided by point cloud, S-VoteNet gathers seed points whose corresponding votes are within the vote groups for seed group generation. Seed groups are then consumed for box size regression and orientation estimation. By decoupling the localization and size estimation, our method effectively reduces the regression pressure of the 3D detector. Experimental results on SUN RGB-D 3D detection benchmark demonstrate that our S-VoteNet achieves state-of-the-art performance by using only point cloud as input.

Video Object Detection Using Object's Motion Context and Spatio-Temporal Feature Aggregation

Jaekyum Kim, Junho Koh, Byeongwon Lee, Seungji Yang, Jun Won Choi

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Auto-TLDR; Video Object Detection Using Spatio-Temporal Aggregated Features and Gated Attention Network

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The deep learning technique has recently led to significant improvement in object-detection accuracy. Numerous object detection schemes have been designed to process each frame independently. However, in many applications, object detection is performed using video data, which consists of a sequence of two-dimensional (2D) image frames. Thus, the object detection accuracy can be improved by exploiting the temporal context of the video sequence. In this paper, we propose a novel video object detection method that exploits both the motion context of the object and spatio-temporal aggregated features in the video sequence to enhance the object detection performance. First, the motion of the object is captured by the correlation between the spatial feature maps of two adjacent frames. Then, the embedding vector, representing the motion context, is obtained by feeding the N correlation maps to long short term memory (LSTM). In addition to generating the motion context vector, the spatial feature maps for N adjacent frames are aggregated to boost the quality of the feature map. The gated attention network is employed to selectively combine only highly correlated feature maps based on their relevance. While most video object detectors are applied to two-stage detectors, our proposed method is applicable to one-stage detectors, which tend to be preferred for practical applications owing to reduced computational complexity. Our numerical evaluation conducted on the ImageNet VID dataset shows that our network offers significant performance gain over baseline algorithms, and it outperforms the existing state-of-the-art one-stage video object detection methods.

Enhancing Deep Semantic Segmentation of RGB-D Data with Entangled Forests

Matteo Terreran, Elia Bonetto, Stefano Ghidoni

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Auto-TLDR; FuseNet: A Lighter Deep Learning Model for Semantic Segmentation

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Semantic segmentation is a problem which is getting more and more attention in the computer vision community. Nowadays, deep learning methods represent the state of the art to solve this problem, and the trend is to use deeper networks to get higher performance. The drawback with such models is a higher computational cost, which makes it difficult to integrate them on mobile robot platforms. In this work we want to explore how to obtain lighter deep learning models without compromising performance. To do so we will consider the features used in the Entangled Random Forest algorithm and we will study the best strategies to integrate these within FuseNet deep network. Such new features allow us to shrink the network size without loosing performance, obtaining hence a lighter model which achieves state-of-the-art performance on the semantic segmentation task and represents an interesting alternative for mobile robotics applications, where computational power and energy are limited.

What and How? Jointly Forecasting Human Action and Pose

Yanjun Zhu, Yanxia Zhang, Qiong Liu, Andreas Girgensohn

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Auto-TLDR; Forecasting Human Actions and Motion Trajectories with Joint Action Classification and Pose Regression

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Forecasting human actions and motion trajectories addresses the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics, while existing works perform them independently. In this paper, we present a method to jointly forecast categories of human action and the pose of skeletal joints in the hope that the two tasks can help each other. As a result, our system can predict not only the future actions but also the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.

Movement-Induced Priors for Deep Stereo

Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala, Arno Solin

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Auto-TLDR; Fusing Stereo Disparity Estimation with Movement-induced Prior Information

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We propose a method for fusing stereo disparity estimation with movement-induced prior information. Instead of independent inference frame-by-frame, we formulate the problem as a non-parametric learning task in terms of a temporal Gaussian process prior with a movement-driven kernel for inter-frame reasoning. We present a hierarchy of three Gaussian process kernels depending on the availability of motion information, where our main focus is on a new gyroscope-driven kernel for handheld devices with low-quality MEMS sensors, thus also relaxing the requirement of having full 6D camera poses available. We show how our method can be combined with two state-of-the-art deep stereo methods. The method either work in a plug-and-play fashion with pre-trained deep stereo networks, or further improved by jointly training the kernels together with encoder--decoder architectures, leading to consistent improvement.

HPERL: 3D Human Pose Estimastion from RGB and LiDAR

Michael Fürst, Shriya T.P. Gupta, René Schuster, Oliver Wasenmüler, Didier Stricker

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Auto-TLDR; 3D Human Pose Estimation Using RGB and LiDAR Using Weakly-Supervised Approach

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In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the performance and leads to very inaccurate absolute pose estimation. With LiDAR sensors becoming more affordable and common on robots and autonomous vehicle setups, we propose an end-to-end architecture using RGB and LiDAR to predict the absolute 3D human pose with unprecedented precision. Additionally, we introduce a weakly-supervised approach to generate 3D predictions using 2D pose annotations from PedX. This allows for many new opportunities in the field of 3D human pose estimation.

A Grid-Based Representation for Human Action Recognition

Soufiane Lamghari, Guillaume-Alexandre Bilodeau, Nicolas Saunier

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Auto-TLDR; GRAR: Grid-based Representation for Action Recognition in Videos

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Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for the task, and are limited in the way they fuse temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets that demonstrate that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.

Vehicle Classification from Profile Measures

Marco Patanè, Andrea Fusiello

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Auto-TLDR; SliceNets: Convolutional Neural Networks for 3D Object Classification of Planar Slices

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This paper proposes two novel convolutional neural networks for 3D object classification, tailored to process point clouds that are composed of planar slices (profiles). In particular, the application that we are targeting is the classification of vehicles by scanning them along planes perpendicular to the driving direction, within the context of Electronic Toll Collection. Depending on sensors configurations, the distance between slices can be measured or not, thus resulting in two types of point clouds, namely metric and non-metric. In the latter case, two coordinates are indeed metric but the third one is merely a temporal index. Our networks, named SliceNets, extract metric information from the spatial coordinates and neighborhood information from the third one (either metric or temporal), thus being able to handle both types of point clouds. Experiments on two datasets collected in the field show the effectiveness of our networks in comparison with state-of-the-art ones.

Delivering Meaningful Representation for Monocular Depth Estimation

Doyeon Kim, Donggyu Joo, Junmo Kim

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Auto-TLDR; Monocular Depth Estimation by Bridging the Context between Encoding and Decoding

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Monocular depth estimation plays a key role in 3D scene understanding, and a number of recent papers have achieved significant improvements using deep learning based algorithms. Most papers among them proposed methods that use a pre-trained network as a deep feature extractor and then decode the obtained features to create a depth map. In this study, we focus on how to use this encoder-decoder structure to deliver meaningful representation throughout the entire network. We propose a new network architecture with our suggested modules to create a more accurate depth map by bridging the context between the encoding and decoding phase. First, we place the pyramid block at the bottleneck of the network to enlarge the view and convey rich information about the global context to the decoder. Second, we suggest a skip connection with the fuse module to aggregate the encoder and decoder feature. Finally, we validate our approach on the NYU Depth V2 and KITTI datasets. The experimental results prove the efficacy of the suggested model and show performance gains over the state-of-the-art model.