Prior Competitions

Argoverse 2023 Challenges

Argoverse hosted four competitions in spring 2023.

• Multi-agent Motion Forecasting using the Argoverse 2 Motion Forecasting Dataset. Given the position, orientation, and category of actors in a scene, predict the future motion of several key actors in the future.
• Unified Detection, Tracking, Forecasting using the Argoverse 2 Sensor Dataset. Detect, Track, and Forecast 26 categories of objects.
• Lidar Scene Flow evaluated using the Argoverse 2 Sensor Dataset, but with training data available in the large, unannotated Argoverse 2 Lidar Dataset.
• 4D Occupancy forecasting using the Argoverse 2 Sensor Dataset. Predict the spacetime occupancy of the world for the next 3 seconds.

Argoverse 2023 Challenge Winners

Congratulations to the winners of the Argoverse 2023 challenges. To learn about the winning methods, check out our presentation from the CVPR 2023 Workshop on Autonomous Driving and read on for detailed reports from the winning teams:

Multi-Agent Motion Forecasting

• QCNet: Query-Centric Trajectory Prediction. Zikang Zhou, Jianping Wang, Yung-Hui Li, Yu-Kai Huang.

End-to-End Forecasting

• 3D Object Detection Challenge Winner. Team Le3DE2E. Kanokphan Lertniphonphan, Feng Chen, Siwei Chen, Jinyao Bao, Pengfei Zheng, Jinbao Zhang, Zhepeng Wang. (Report)
• 3D MOT Challenge Winner. Team Le3DE2E. Feng Chen, Kanokphan Lertniphonphan, Siwei Chen, Jinyao Bao, Pengfei Zheng, Jinbao Zhang, Zhepeng Wang. (Report)
• 3D MOT Challenge Honorable Mention. Team AutoDrive. Jae-Keun Lee, Jin-Hee Lee, Joohyun Lee, Heechul Jung, Soon Kwon. (Report)
• End-to-End Forecasting Challenge Winner. Team Le3DE2E. Zhepeng Wang, Feng Chen, Kanokphan Lertniphonphan, Siwei Chen, Jinyao Bao, Pengfei Zheng, Jinbao Zhang, Kaer Huang, Tao Zhang. (Report)
• End-to-End Forecasting Challenge Honorable Mention. Team DGIST-CVLab. Jungwan Woo, Jaeyeul Kim, Sunghoon Im. (Report)

4D Occupancy Forecasting

• Team Le3DE2E_Occ. Pengfei Zheng, Kanokphan Lertniphonphan, Feng Chen, Siwei Chen, Bingchuan Sun, Jun Xie, Zhepeng Wang.

Self-supervised Scene Flow

• ZeroFlow. Kyle Vedder, Neehar Peri, Nathaniel Chodosh, Ishan Khatri, Eric Eaton, Dinesh Jayaraman, Yang Liu, Deva Ramanan, James Hays. (Paper, Github)

Argoverse 2022 Challenge Winners

Congratulations to the winners of the Argoverse 2022 challenges. To learn about the winning methods, check out our presentation from the CVPR 2022 Workshop on Autonomous Driving and read on for detailed reports from the winning teams:

3D Object Detection

• Detectors. Jin Fang*, Qinghao Meng*, Dingfu Zhou, Chulin Tang, Jianbing Shen, Cheng-Zhong Xu and Liangjun Zhang. Robotics and Autonomous Driving Laboratory, Baidu Research, University of Macau, Beijing Institute of Technology, University of California, Irvine. (Report)

Stereo Depth Estimation

• GMStereo. Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao. Department of Data Science and AI, Monash University, The University of Sydney, JD Explore Academy. (Based on GMFlow)
• MSCLab. Antyanta Bangunharcana, Soohyun Kim, Kyung-Soo Kim. Korea Advanced Institute of Science and Technology. (Report)
• LRM. Luis Rosero, Fernando Osório. Institute of Mathematics and Computer Science, University of São Paulo. (Report)

Motion Forecasting

• BANet. Chen Zhang, Honglin Sun, Chen Chen, Yandong Guo. OPPO Research Institute, Waseda University. (Report)
• QML. Tong Su, Xishun Wang, Xiaodong Yang. QCraft AI. (Report)
• GANet. Mingkun Wang, Changqian Yu, Mingxu Wang, Dongchun Ren, Deheng Qian. Peking University, Meituan, Fudan University. (Report)