Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

26 Aug 2019Benjin ZhuZhengkai JiangXiangxin ZhouZeming LiGang Yu

This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convolution to extract rich semantic features, which are then fed into a class-balanced multi-head network to perform 3D object detection... (read more)

PDF Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
3D Object Detection nuScenes MEGVII NDS 63.3 # 2
MAP 52.8 # 2

Methods used in the Paper