  In this technical report, we introduce our winning solution "HorizonLiDAR3D"
for the 3D detection track and the domain adaptation track in Waymo Open
Dataset Challenge at CVPR 2020. Many existing 3D object detectors include
prior-based anchor box design to account for different scales and aspect ratios
and classes of objects, which limits its capability of generalization to a
different dataset or domain and requires post-processing (e.g. Non-Maximum
Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point
cloud object detector AFDet, using object key-points to encode the 3D
attributes, and to learn an end-to-end point cloud object detection without the
need of hand-engineering or learning the anchors. AFDet serves as a strong
baseline in our winning solution and significant improvements are made over
this baseline during the challenges. Specifically, we design stronger networks
and enhance the point cloud data using densification and point painting. To
leverage camera information, we append/paint additional attributes to each
point by projecting them to camera space and gathering image-based perception
information. The final detection performance also benefits from model ensemble
and Test-Time Augmentation (TTA) in both the 3D detection track and the domain
adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and
69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation
track.
