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.

介绍了一种无锚点、无 NMS 的 3D 点云目标检测器，使用对象关键点编码 3D 属性实现端到端检测，利用数据增强和点着色以及投影到相机空间和图像感知信息的附加属性来增强性能，并通过模型集成和 TTA 在 3D 检测和域自适应路线上实现了第一名。