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In this technical report, we introduce our winning solution "HorizonLiDAR3D"for the 3D detection track and the domain adaptation track in Waymo OpenDataset Challenge at CVPR 2020. Many existing 3D object detectors includeprior-based anchor box design to account for different scales and aspect ratiosand classes of objects, which limits its capability of generalization to adifferent dataset or domain and requires post-processing (e.g. Non-MaximumSuppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D pointcloud object detector AFDet, using object key-points to encode the 3Dattributes, and to learn an end-to-end point cloud object detection without theneed of hand-engineering or learning the anchors. AFDet serves as a strongbaseline in our winning solution and significant improvements are made overthis baseline during the challenges. Specifically, we design stronger networksand enhance the point cloud data using densification and point painting. Toleverage camera information, we append/paint additional attributes to eachpoint by projecting them to camera space and gathering image-based perceptioninformation. The final detection performance also benefits from model ensembleand Test-Time Augmentation (TTA) in both the 3D detection track and the domainadaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptationtrack.

1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation