3d detection papers

Author: Li Heyuan (李贺元)
Email: [email protected]
The papers in this list are about Autonomous Vehicles 3d detection & semantic segmentation
especially those using point clouds and in deep learning methods.
All rights reserved

If you have any suggestion or want to recommend new papers, please feel free to let me know.
I have read most of the papers here, and am very happy to discuss with you if you have any issues on these papers.
I will keep updating this project continuously.

I will improve this project in a few days.

todo list:

1. links -- done
2. recommended papers -- done. The ones that I particularly liked are marked with ⭐.
3. authors -- done
4. models -- remove this item
5. relevant code -- done
6. rank by year -- done
7. active researchers -- done
8. write a brief review for each mainstream method
9. try to write some reviews for the recommended papers, if I have time.

Mainstream Method Series

• Voxel
• Multi-view
• Pointnet
• Other

Voxel

• Sliding shapes for 3d object detection in depth images, Shuran Song, Jianxiong Xiao, code, website, ECCV, 2014
• Voxnet: A 3d convolutional neural network for real-time object recognition, Daniel Maturana and Sebastian Scherer, code, website, IROS, 2015
• Deep sliding shapes for amodal 3d object detection in rgb-d images, Shuran Song, Jianxiong Xiao, code, website, CVPR, 2016
• Octnet: Learning deep 3d representations at high resolutions, Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger, code, CVPR, 2017 ⭐
• 3d fully convolutional network for vehicle detection in point cloud, Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger, unofficial code, IROS, 2017
• Voting for voting in online point cloud object detection, Dominic Zeng Wang, Ingmar Posner, Robotics: Science and System, 2015
• Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks, Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner, ICRA, 2017

Multi-view

• Pedestrian detection combining RGB and dense LIDAR data, Cristiano Premebida, João Carreira, Jorge Batista, Urbano Nunes, IROS, 2014
• 3d-assisted feature synthesis for novel views of an object, Hao Su, Fan Wang, Eric Yi, Leonidas Guibas, ICCV, 2015
• Multiview random forest of local experts combining rgb and lidar data for pedestrian detection, A. González, G. Villalonga, J. Xu, D. Vázquez, J. Amores, and A. López., IEEE Intelligent Vehicles Symposium, 2015
• Multiview convolutional neural networks for 3d shape recognition, Hang Su Subhransu Maji Evangelos Kalogerakis Erik Learned-Miller, code ICCV, 2015
• Vehicle detection from 3d lidar using fully convolutional network, Bo Li, Tianlei Zhang, Tian Xia, Robotics: Science and System, 2016
• Volumetric and multi-view cnns for object classification on 3d data, Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, Leonidas J. Guibas, CVPR, 2016
• Fusionnet: 3d object classification using multiple data representations, Vishakh Hegde, Reza Zadeh, CoRR, 2016
• Multi-view 3d object detection network for autonomous driving, Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia, code, CVPR, 2017 ⭐
• Joint 3d proposal generation and object detection from view aggregation, Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, Steven L. Waslander, code, IROS, 2018
• Pixor: Real-time 3d object detection from point clouds, Bin Yang, Wenjie Luo, Raquel Urtasun, CVPR, 2018 ⭐
• Deep continuous fusion for multi-sensor 3d object detection, Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun, ECCV, 2018 ⭐
• MLOD: A multi-view 3D object detection based on robust feature fusion method Jian Deng and Krzysztof Czarnecki, 2019

PointNet

• Pointnet: Deep learning on point sets for 3d classification and segmentation, Charles R. Qi*, Hao Su*, Kaichun Mo, Leonidas J. Guibas, code, website, CVPR, 2017 ⭐
• Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas, code, website, NIPS, 2017
• Frustum pointnets for 3d object detection from rgb-d data, Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas, code, website, CVPR, 2018 ⭐
• PointRCNN 3D Object Proposal Generation and Detection from Point Cloud, Shaoshuai Shi, Xiaogang Wang, Hongsheng Li, 2018 ⭐
• Pointfusion: Deep sensor fusion for 3d bounding box estimation, Danfei Xu, Dragomir Anguelov, Ashesh Jain, CVPR, 2018
• Voxelnet: End-to-end learning for point cloud based 3d object detection, Yin Zhou, Oncel Tuzel, unofficial code, CVPR, 2018 ⭐
• Second: Sparsely embedded convolutional detection, Yan Yan,, Yuxing Mao, and Bo Li, code, Sensors, 2018
• Pointpillars: Encoders for object detection from point clouds, Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom, code, 2018
• RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement, Kiwoo Shin, Youngwook Paul Kwon, Masayoshi Tomizuka, code, 2018 ⭐
• IPOD: Intensive Point-based Object Detector for Point Cloud, Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, Jiaya Jia, 2018
• Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection, Zhixin Wang, Kui Jia, 2019 ⭐
• 3D Object Detection Using Scale Invariant and Feature Reweighting Networks, Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang, AAAI, 2019
• STD: Sparse-to-Dense 3D Object Detector for Point CloudZetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, Jiaya Jia, ICCV, 2019 ⭐
• Patch Refinement - Localized 3D Object Detection Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, Sepp Hochreiter, 2019

Other

• Recurrent slice networks for 3d segmentation of point clouds, Qiangui Huang, Weiyue Wang, Ulrich Neumann, code, CVPR, 2018 ⭐
• Pointsift: A sift-like network module for 3d point cloud semantic segmentation, Mingyang Jiang, Yiran Wu, Tianqi Zhao, Zelin Zhao, Cewu Lu, code, CoRR, 2018
• A General Pipeline for 3D Detection of Vehicles Xinxin Du ; Marcelo H. Ang ; Sertac Karaman ; Daniela Rus, 2018
• Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud,Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li, 2019 ⭐
• Multi-Task Multi-Sensor Fusion for 3D Object Detection, Ming Liang, Bin Yang, Yun Chen, Rui Hu, Raquel Urtasun, 2019, ⭐
• Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression, Hamid Rezatofighi, Nathan Tsoi1, JunYoung Gwak, Amir Sadeghian, Ian Reid, Silvio Savarese, 2019, ⭐
• Voxel-FPN: multi-scale voxel feature aggregationin 3D object detection from point clouds, Bei Wang, Jianping An and Jiayan Cao, 2019, ⭐
• PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, Shaoshuai Shi, Xiaogang Wang, Hongsheng Li, 2019
• Fast Point R-CNN, Yilun Chen, Shu Liu, Xiaoyong Shen, Jiaya Jia, 2019
• Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes Xuesong Li, Jose Guivant, Ngaiming Kwok, Yongzhi Xu, Ruowei Li, Hongkun Wu, 2019
• SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud ZHIYU WANG, HAO FU, LI WANG, LIANG XIAO, AND BIN DA, 2019
• PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module Liang Xie, Chao Xiang, Zhengxu Yu, Guodong Xu, Zheng Yang, Deng Cai1, Xiaofei He, AAAI, 2019
• [HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection] (https://arxiv.org/pdf/2003.00186.pdf) Maosheng Ye, Shuangjie Xu, Tongyi Cao, CVPR, 2020
• [FPConv: Learning Local Flattening for Point Convolution] (https://arxiv.org/pdf/2002.10701.pdf) Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han, CVPR, 2020
• [Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection] (https://arxiv.org/pdf/2002.10701.pdf) Liang Du, Xiaoqing Ye, Xiao Tan, Jianfeng Feng, Zhenbo Xu, Errui Ding and Shilei Wen, CVPR, 2020
• [SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds] (https://arxiv.org/pdf/2006.04043.pdf) Qingdong He, Zhengning Wang, Hao Zeng, Yi Zeng, Shuaicheng Liu, Bing Zeng, CVPR, 2020
• [H3DNet: 3D Object Detection Using Hybrid Geometric Primitives] (https://arxiv.org/pdf/2006.05682.pdf) Zaiwei Zhang, Bo Sun, Haitao Yang, Qixing Huang