  In this technical report, we introduce our submission to the Waymo 3D
Detection leaderboard. Our network is based on the Centerpoint architecture,
but with significant improvements. We design a 2D backbone to utilize
multi-scale features for better detecting objects with various sizes, together
with an optimal transport-based target assignment strategy, which dynamically
assigns richer supervision signals to the detection candidates. We also apply
test-time augmentation and model-ensemble for further improvements. Our
submission currently ranks 4th place with 78.45 mAPH on the Waymo 3D Detection
leaderboard.
