Multi-Sensor 3D Object Box Refinement for Autonomous Driving

We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving. In our framework, the monocular camera serves as the fundamental sensor for 2D object proposal and initial 3D bounding box prediction. While the stereo cameras and LiDAR are treated as adaptive plug-in sensors to refine the 3D box localization performance. For each observed element in the raw measurement domain (e.g., pixels for stereo, 3D points for LiDAR), we model the local geometry as an instance vector representation, which indicates the 3D coordinate of each element respecting to the object frame. Using this unified geometric representation, the 3D object location can be unified refined by the stereo photometric alignment or point cloud alignment. We demonstrate superior 3D detection and localization performance compared to state-of-the-art monocular, stereo methods and competitive performance compared with the baseline LiDAR method on the KITTI object benchmark.

[1]  Yan Lu,et al.  MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization , 2018, AAAI.

[2]  Yin Zhou,et al.  VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Bo Li,et al.  SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Sanja Fidler,et al.  3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ji Wan,et al.  Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Leonidas J. Guibas,et al.  Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Shaojie Shen,et al.  Stereo R-CNN Based 3D Object Detection for Autonomous Driving , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Bin Yang,et al.  Multi-Task Multi-Sensor Fusion for 3D Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Roberto Cipolla,et al.  Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Shaojie Shen,et al.  Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving , 2018, ECCV.

[13]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Zhixin Wang,et al.  Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[16]  Bo Li,et al.  3D fully convolutional network for vehicle detection in point cloud , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[17]  James M. Rehg,et al.  3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[18]  Andreas Geiger,et al.  Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[20]  Leonidas J. Guibas,et al.  Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Yan Wang,et al.  Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Bin Yang,et al.  Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Jana Kosecka,et al.  3D Bounding Box Estimation Using Deep Learning and Geometry , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Steven Lake Waslander,et al.  Joint 3D Proposal Generation and Object Detection from View Aggregation , 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Konrad Schindler,et al.  Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Xiaogang Wang,et al.  PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Bin Yang,et al.  Deep Continuous Fusion for Multi-sensor 3D Object Detection , 2018, ECCV.

[29]  Dushyant Rao,et al.  Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[30]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Sanja Fidler,et al.  Monocular 3D Object Detection for Autonomous Driving , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Danfei Xu,et al.  PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[33]  Bin Yang,et al.  PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[34]  Zengyi Qin,et al.  Triangulation Learning Network: From Monocular to Stereo 3D Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Steven L. Waslander,et al.  Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Bin Xu,et al.  Multi-level Fusion Based 3D Object Detection from Monocular Images , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[37]  Tian Xia,et al.  Vehicle Detection from 3D Lidar Using Fully Convolutional Network , 2016, Robotics: Science and Systems.

[38]  Jiong Yang,et al.  PointPillars: Fast Encoders for Object Detection From Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Thierry Chateau,et al.  Deep MANTA: A Coarse-to-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis from Monocular Image , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).