Shaoshuai Shi Postdoctoral Researcher Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Email: shaoshuaics [at] gmail [dot] com [Google Scholar]   [GitHub]

About Me

I am a postdoctoral researcher at Max Planck Institute for Informatics (MPI-INF), working with Prof. Bernt Schiele. I obtained my Ph.D. degree from Multimedia Lab (MMLab) of The Chinese University of Hong Kong (CUHK), supervised by Prof. Xiaogang Wang and Prof. Hongsheng Li. Before that, I received my bachelor’s degree from the Computer Science Honor Class of Harbin Institute Technology (HIT).

My research interests focus on computer vision and deep learning, especially the 3D scene understanding, object detection and autonomous driving / robotics related topics.

News

• [2022/07] One paper accepted by ECCV 2022.
• [2022/06] Champion of Waymo Open Dataset Motion Prediction challenge 2022.
• [2022/03] One paper accepted by CVPR 2022.
• [2022/01] The source code of PV-RCNN++ has been released to OpenPCDet.
• [2021/07] Two papers accepted by ICCV 2021.
• [2021/07] I was awarded World Artificial Intelligence Conference Rising Star Award, see the news from WAIC.
• [2021/03] Two papers accepted by CVPR 2021.
• [2020/10] I was awarded Google PhD Fellowship 2020, see the news from Google AI Blog.
• [2020/06] Highly optimized PyTorch codebase OpenPCDet for LiDAR-based 3D scene understanding is released.
• [2020/06] We won 1st place among all LiDAR-only methods / 2nd place among all methods on 3D Detection, 3D Tracking, Domain Adaptation three tracks of Waymo Open Dataset challenge. See the official blog post from Waymo.
• [2020/03] Two papers with one oral accepted by CVPR 2020.
• [2020/02] One paper (Part-A2-Net) accepted by IEEE TPAMI.

Codebase

OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Cloud OpenPCDet Development Team June 2020 [Code]   [Bibtex]

@misc{openpcdet2020,
    title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
    author={OpenPCDet Development Team},
    howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}},
    year={2020}
}

Publications

*: Equal Contribution, †: Corresponding Author

• Towards Efficient 3D Object Detection with Knowledge Distillation
  Jihan Yang, Shaoshuai Shi, Runyu Ding, Zhe Wang, Xiaojuan Qi
  Preprint, arXiv:2205.15156.

• MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection
  Xuesong Chen*, Shaoshuai Shi*†, Benjin Zhu, Ka Chun Cheung, Hang Xu, Hongsheng Li†
  European Conference on Computer Vision (ECCV), 2022.
  Ranked 1st place on Waymo 3D object detection leaderboard (June, 2022).

• RBGNet: Ray-based Grouping for 3D Object Detection
  Haiyang Wang, Shaoshuai Shi† , Ze Yang, Rongyao Fang, Qi Qian, Hongsheng Li, Bernt Schiele, Liwei Wang
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  

• LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector
  Xiaoyang Guo, Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
  International Conference on Computer Vision (ICCV), 2021.  [Code]  

• Guided Point Contrastive Learning for Semi-Supervised Point Cloud Semantic Segmentation
  Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, Jiaya Jia
  International Conference on Computer Vision (ICCV), 2021.

• ST3D++: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
  Jihan Yang, Shaoshuai Shi, Zhe Wang, Hongsheng Li, Xiaojuan Qi
  Preprint, arXiv:2108.06682.

• PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
  Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, Hongsheng Li
  Preprint, arXiv:2102.00463.  [Code]  

• ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
  Jihan Yang*, Shaoshuai Shi*, Zhe Wang, Hongsheng Li, Xiaojuan Qi
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [Code]  

• Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
  Bowen Cheng, Lu Sheng, Shaoshuai Shi, Ming Yang, Dong Xu
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [Code]  

• Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
  Jiajun Deng, Shaoshuai Shi, Peiwei Li, Wengang Zhou, Yanyong Zhang, Houqiang Li
  AAAI Conference on Artificial Intelligence (AAAI), 2021.  [Code]  

• The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges
  Shaoshuai Shi, Chaoxu Guo, Jihan Yang, Hongsheng Li
  Technical report of top-performing LiDAR-only solutions to Waymo Open Dataset Challenges at Workshop of CVPR 2020.  [Code]  

• PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
  Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [Code]  
  Ranked 1st place on KITTI 3D object detection benchmark (Car, Nov 2019 - Aug 2020).

• PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
  Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Oral]  [Code]  
  Ranked 1st place on ScanNet 3D Semantic instance benchmark (Nov-16 2019).

• From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
  Shaoshuai Shi, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li
  IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), accepted.   [Code]
  Ranked 1st place on KITTI 3D object detection benchmark (Car, July-9 2019).

• SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
  Hongwei Yi, Shaoshuai Shi, Mingyu Ding, Jiankai Sun, Kui Xu, Hui Zhou, Zhe Wang, Sheng Li, Guoping Wang
  International Conference on Robotics and Automation (ICRA), 2020.

• PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
  Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.   [Code-v1] [Code-v2]
  The top-10 cited papers among all CVPR-2019 papers (March, 2021), refer to here.

• Feature Intertwiner for Object Detection
  Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang
  International Conference on Learning Representation (ICLR), 2019.

• GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction
  Li Jiang, Shaoshuai Shi, Xiaojuan Qi, Jiaya Jia
  European Conference on Computer Vision (ECCV), 2018. [Oral]

• FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates
  Yijin Guan, Hao Liang, Ningyi Xu, Wenqiang Wang, Shaoshuai Shi, Xi Chen, Guangyu Sun, Wei Zhang, Jason Cong
  IEEE Field-Programmable Custom Computing Machines (FCCM), 2017.

Experience

• Postdoc Researcher, Oct 2021 - Now, Max Planck Institute for Informatics, Germany.
  Advised by Prof. Bernt Schiele.

• Research Intern, Mar 2019 - Feb 2021, Autonomous Driving Group of SenseTime, Shenzhen, China.
  Working with Dr. Zhe Wang and Dr. Jianping Shi.

• Research Intern, July 2016 - July 2017, System Group of Microsoft Research Asia (MSRA), Beijing, China.
  Advised by Prof. Ningyi Xu.

Honors & Awards

• Champion of Waymo Open Dataset Motion Prediction challenge, 2022.
• World Artificial Intelligence Conference (WAIC) Rising Star Award (17 selected world-wide), 2021.
• Google PhD Fellowship in machine perception (10 selected world-wide), 2020.
• Hong Kong PhD Fellowship (The highest scholarship for PhD students in Hong Kong), 2017-2021
• National Scholarship, 2014, 2015, 2016
• Silver Prize of China Collegiate Programming Contest (CCPC 2015), 2015
• Second Prize of China Undergraduate Mathematical Contest in Modeling (CUMCM 2015), 2015
• Gold Prize of China Collegiate Programming Contest (CCPC) of Northeast Area, 2015