PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

CVPR 2019 Shaoshuai ShiXiaogang WangHongsheng Li

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
3D Object Detection KITTI Cars Easy PointRCNN AP 84.32% # 9
Object Detection KITTI Cars Easy PointRCNN Shi et al. (2019) AP 85.94 # 2
Object Detection KITTI Cars Hard PointRCNN Shi et al. (2019) AP 68.32 # 2
3D Object Detection KITTI Cars Hard PointRCNN AP 67.86% # 8
3D Object Detection KITTI Cars Moderate PointRCNN AP 75.42% # 8
Object Detection KITTI Cars Moderate PointRCNN Shi et al. (2019) AP 75.76 # 2
3D Object Detection KITTI Cyclists Easy PointRCNN AP 73.93% # 6
3D Object Detection KITTI Cyclists Hard PointRCNN AP 53.59% # 5
3D Object Detection KITTI Cyclists Moderate PointRCNN AP 59.60% # 5

Methods used in the Paper


METHOD TYPE
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