3DSSD: Point-Based 3D Single Stage Object Detector
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Yanan Sun | Jiaya Jia | Zetong Yang | Shu Liu | Jiaya Jia | Yanan Sun | Shu Liu | Zetong Yang
[1] 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).
[2] Ingmar Posner,et al. Voting for Voting in Online Point Cloud Object Detection , 2015, Robotics: Science and Systems.
[3] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[4] Cristiano Premebida,et al. Pedestrian detection combining RGB and dense LIDAR data , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[5] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[6] Xiaogang Wang,et al. Part-A2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud , 2019, ArXiv.
[7] Xiaoyong Shen,et al. STD: Sparse-to-Dense 3D Object Detector for Point Cloud , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[8] Laurens van der Maaten,et al. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[9] Bo Li,et al. SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.
[10] Ji Wan,et al. Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] 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).
[12] Bin Yang,et al. PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] 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.
[15] 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).
[16] Shu Liu,et al. IPOD: Intensive Point-based Object Detector for Point Cloud , 2018, ArXiv.
[17] Stephen Lin,et al. RepPoints: Point Set Representation for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Sepp Hochreiter,et al. Patch Refinement - Localized 3D Object Detection , 2019, ArXiv.
[19] Leonidas J. Guibas,et al. Deep Hough Voting for 3D Object Detection in Point Clouds , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] 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).
[21] Bin Yang,et al. Deep Continuous Fusion for Multi-sensor 3D Object Detection , 2018, ECCV.
[22] 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).
[23] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[24] Qiang Xu,et al. nuScenes: A Multimodal Dataset for Autonomous Driving , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Vincent Lepetit,et al. Multiple 3D Object tracking for augmented reality , 2008, 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality.
[26] Xiaogang Wang,et al. From Points to Parts: 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] C. Qi. Deep Learning on Point Sets for 3 D Classification and Segmentation , 2016 .
[28] Jiaya Jia,et al. Fast Point R-CNN , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[29] Hao Chen,et al. FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[30] 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).
[31] 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).
[32] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[33] 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).
[34] 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.
[35] 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.
[36] Jiaolong Xu,et al. Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).