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https://github.com/lhyfst/3d-detection-papers

The papers in this list are about 3d detection, especially those using point clouds.
https://github.com/lhyfst/3d-detection-papers

3d-detection cloud-point paper read-list

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The papers in this list are about 3d detection, especially those using point clouds.

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# 3d detection papers



> Author: Li Heyuan (李贺元)


> Email: lhyfst@gmail.com


> 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 :star:. 

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](http://slidingshapes.cs.princeton.edu/paper.pdf), 

Shuran Song, Jianxiong Xiao, 

[code](http://slidingshapes.cs.princeton.edu/), 

[website](http://slidingshapes.cs.princeton.edu/), 

ECCV, 2014

- [Voxnet: A 3d convolutional neural network for real-time object recognition](https://www.ri.cmu.edu/pub_files/2015/9/voxnet_maturana_scherer_iros15.pdf), 

Daniel Maturana and Sebastian Scherer,

[code](https://github.com/dimatura/voxnet), 

[website](http://dimatura.net/research/voxnet/), 

IROS, 2015 

- [Deep sliding shapes for amodal 3d object detection in rgb-d images](http://dss.cs.princeton.edu/paper.pdf), 

Shuran Song, Jianxiong Xiao, 

[code](https://github.com/shurans/DeepSlidingShape), 

[website](http://dss.cs.princeton.edu/), 

CVPR, 2016

- [Octnet: Learning deep 3d representations at high resolutions](https://arxiv.org/pdf/1611.05009.pdf), 

Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger, 

[code](https://github.com/griegler/octnet), 

CVPR, 2017  :star:

- [3d fully convolutional network for vehicle detection in point cloud](https://arxiv.org/pdf/1611.08069.pdf), 

Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger, 

[unofficial code](https://github.com/yukitsuji/3D_CNN_tensorflow), 

IROS, 2017

- [Voting for voting in online point cloud object detection](http://www.robots.ox.ac.uk/~mobile/Papers/2015RSS_wang.pdf), 

Dominic Zeng Wang, Ingmar Posner,

Robotics: Science and System, 2015

- [Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks](https://arxiv.org/pdf/1609.06666),

Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner, 

ICRA, 2017



## Multi-view

- [Pedestrian detection combining RGB and dense LIDAR data](https://ieeexplore.ieee.org/document/6943141), 

Cristiano Premebida, João Carreira, Jorge Batista, Urbano Nunes, 

IROS, 2014

- [3d-assisted feature synthesis for novel views of an object](http://openaccess.thecvf.com/content_iccv_2015/papers/Su_3D-Assisted_Feature_Synthesis_ICCV_2015_paper.pdf), 

Hao Su, Fan Wang, Eric Yi, Leonidas Guibas, 

ICCV, 2015

- [Multiview random forest of local experts combining rgb and lidar data for pedestrian detection](https://ieeexplore.ieee.org/document/7225711), 

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](http://vis-www.cs.umass.edu/mvcnn/docs/su15mvcnn.pdf), 

Hang Su Subhransu Maji Evangelos Kalogerakis Erik Learned-Miller, 

[code](https://github.com/jongchyisu/mvcnn_pytorch)

ICCV, 2015

- [Vehicle detection from 3d lidar using fully convolutional network](https://arxiv.org/pdf/1608.07916.pdf), 

Bo Li, Tianlei Zhang, Tian Xia, 

Robotics: Science and System, 2016

- [Volumetric and multi-view cnns for object classification on 3d data](http://openaccess.thecvf.com/content_cvpr_2016/papers/Qi_Volumetric_and_Multi-View_CVPR_2016_paper.pdf), 

Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, Leonidas J. Guibas, 

CVPR, 2016

- [Fusionnet: 3d object classification using multiple data representations](https://arxiv.org/pdf/1607.05695.pdf), 

Vishakh Hegde, Reza Zadeh, 

CoRR, 2016

- [Multi-view 3d object detection network for autonomous driving](https://arxiv.org/pdf/1611.07759.pdf), 

Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia, 

[code](https://github.com/bostondiditeam/MV3D), 

CVPR, 2017  :star:

- [Joint 3d proposal generation and object detection from view aggregation](https://arxiv.org/pdf/1712.02294.pdf), 

Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, Steven L. Waslander, 

[code](https://github.com/kujason/avod), 

IROS, 2018

- [Pixor: Real-time 3d object detection from point clouds](https://arxiv.org/pdf/1902.06326), 

Bin Yang, Wenjie Luo, Raquel Urtasun, 

CVPR, 2018  :star:

- [Deep continuous fusion for multi-sensor 3d object detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf), 

Ming Liang, Bin Yang, Shenlong Wang, and Raquel Urtasun, 

ECCV, 2018  :star:

- [MLOD: A multi-view 3D object detection based on robust feature fusion method](https://arxiv.org/pdf/1909.04163.pdf)

Jian Deng and Krzysztof Czarnecki, 

2019



## PointNet

- [Pointnet: Deep learning on point sets for 3d classification and segmentation](https://arxiv.org/abs/1612.00593), 

Charles R. Qi*, Hao Su*, Kaichun Mo, Leonidas J. Guibas, 

[code](https://github.com/charlesq34/pointnet), 

[website](http://stanford.edu/~rqi/pointnet/), 

CVPR, 2017  :star:

- [Pointnet++: Deep hierarchical feature learning on point sets in a metric space](https://arxiv.org/abs/1706.02413), 

Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas, 

[code](https://github.com/charlesq34/pointnet2), 

[website](http://stanford.edu/~rqi/pointnet2/), 

NIPS, 2017

- [Frustum pointnets for 3d object detection from rgb-d data](https://arxiv.org/abs/1711.08488), 

Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas, 

[code](https://github.com/charlesq34/frustum-pointnets), 

[website](http://stanford.edu/~rqi/frustum-pointnets/), 

CVPR, 2018  :star:

- [PointRCNN 3D Object Proposal Generation and Detection from Point Cloud](https://arxiv.org/pdf/1812.04244.pdf), 

Shaoshuai Shi, Xiaogang Wang, Hongsheng Li, 

2018  :star:

- [Pointfusion: Deep sensor fusion for 3d bounding box estimation](https://arxiv.org/pdf/1711.10871.pdf), 

Danfei Xu, Dragomir Anguelov, Ashesh Jain, 

CVPR, 2018

- [Voxelnet: End-to-end learning for point cloud based 3d object detection](https://arxiv.org/pdf/1711.06396.pdf), 

Yin Zhou, Oncel Tuzel, 

[unofficial code](https://github.com/jeasinema/VoxelNet-tensorflow), 

CVPR, 2018  :star:

- [Second: Sparsely embedded convolutional detection](https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf), 

Yan Yan,, Yuxing Mao, and Bo Li, 

[code](https://github.com/traveller59/second.pytorch), 

Sensors, 2018

- [Pointpillars: Encoders for object detection from point clouds](https://arxiv.org/pdf/1812.05784.pdf), 

Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom, 

[code](https://github.com/nutonomy/second.pytorch), 

2018

- [RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement](https://arxiv.org/pdf/1811.03818), 

Kiwoo Shin, Youngwook Paul Kwon, Masayoshi Tomizuka, 

[code](https://github.com/Kiwoo/RoarNet), 

2018  :star:

- [IPOD: Intensive Point-based Object Detector for Point Cloud](https://arxiv.org/pdf/1812.05276.pdf), 

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](https://arxiv.org/pdf/1903.01864.pdf), 

Zhixin Wang, Kui Jia, 

2019  :star:

- [3D Object Detection Using Scale Invariant and Feature Reweighting Networks](https://arxiv.org/pdf/1711.10871.pdf), 

Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang, 

AAAI, 2019

- [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](https://arxiv.org/pdf/1907.10471.pdf)Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, Jiaya Jia, 

ICCV, 2019  :star:

- [Patch Refinement - Localized 3D Object Detection](https://arxiv.org/pdf/1910.04093.pdf)

Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, Sepp Hochreiter, 

2019 



## Other

- [Recurrent slice networks for 3d segmentation of point clouds](https://arxiv.org/pdf/1802.04402.pdf), 

Qiangui Huang, Weiyue Wang, Ulrich Neumann, 

[code](https://github.com/qianguih/RSNet), 

CVPR, 2018  :star:

- [Pointsift: A sift-like network module for 3d point cloud semantic segmentation](https://arxiv.org/abs/1807.00652), 

Mingyang Jiang, Yiran Wu, Tianqi Zhao, Zelin Zhao, Cewu Lu, 

[code](https://github.com/MVIG-SJTU/pointSIFT), 

CoRR, 2018

- [A General Pipeline for 3D Detection of Vehicles](https://ieeexplore.ieee.org/abstract/document/8461232)

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](https://arxiv.org/pdf/1907.03670.pdf),Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li, 

2019  :star:

- [Multi-Task Multi-Sensor Fusion for 3D Object Detection](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liang_Multi-Task_Multi-Sensor_Fusion_for_3D_Object_Detection_CVPR_2019_paper.pdf), 

Ming Liang, Bin Yang, Yun Chen, Rui Hu, Raquel Urtasun,

2019, :star:

- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://arxiv.org/pdf/1902.09630.pdf),

Hamid Rezatofighi, Nathan Tsoi1, JunYoung Gwak, Amir Sadeghian, Ian Reid, Silvio Savarese,

2019, :star:

- [Voxel-FPN: multi-scale voxel feature aggregationin 3D object detection from point clouds](https://arxiv.org/ftp/arxiv/papers/1907/1907.05286.pdf),

Bei Wang, Jianping An and Jiayan Cao,

2019, :star:

- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_PointRCNN_3D_Object_Proposal_Generation_and_Detection_From_Point_Cloud_CVPR_2019_paper.pdf),

Shaoshuai Shi, Xiaogang Wang, Hongsheng Li,

2019

- [Fast Point R-CNN](https://arxiv.org/pdf/1908.02990.pdf),

Yilun Chen, Shu Liu, Xiaoyong Shen, Jiaya Jia, 

2019

- [Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes](https://arxiv.org/pdf/1901.08373.pdf)

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](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8813061)

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](https://arxiv.org/pdf/1911.06084.pdf)

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