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In this technical report, we present the top-performing LiDAR-only solutionsfor 3D detection, 3D tracking and domain adaptation three tracks in Waymo OpenDataset Challenges 2020. Our solutions for the competition are built upon ourrecent proposed PV-RCNN 3D object detection framework. Several variants of ourPV-RCNN are explored, including temporal information incorporation, dynamicvoxelization, adaptive training sample selection, classification with RoIfeatures, etc. A simple model ensemble strategy with non-maximum-suppressionand box voting is adopted to generate the final results. By using only LiDARpoint cloud data, our models finally achieve the 1st place among all LiDAR-onlymethods, and the 2nd place among all multi-modal methods, on the 3D Detection,3D Tracking and Domain Adaptation three tracks of Waymo Open DatasetChallenges. Our solutions will be available athttps://github.com/open-mmlab/OpenPCDet

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