Corpus ID: 221370703

PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges

@article{Shi2020PVRCNNTT,
  title={PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges},
  author={Shaoshuai Shi and Chaoxu Guo and Jihan Yang and Hongsheng Li},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.12599}
}
In this technical report, we present the top-performing LiDAR-only solutions for 3D detection, 3D tracking and domain adaptation three tracks in Waymo Open Dataset Challenges 2020. Our solutions for the competition are built upon our recent proposed PV-RCNN 3D object detection framework. Several variants of our PV-RCNN are explored, including temporal information incorporation, dynamic voxelization, adaptive training sample selection, classification with RoI features, etc. A simple model… Expand

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RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection

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