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Research map / Detection and segmentation / 3D detection and driving

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

AAAI 2021 · Detection and segmentation › 3D detection and driving · approach: Unified LiDAR 3D detection · 1,004 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add 3D voxel RoI refinement with a detect head to the BEV one-stage detector (ablation)Voxel R-CNN · KITTI val, Car3D AP Moderate: 77.99 -> 84.21Helped
Add a detect head using ball query and original PointNet aggregation (ablation)BEV one-stage baseline · KITTI val, CarFPS: 40.8 -> 17.4Hurt
Replace ball query with voxel query (ablation)Voxel R-CNN detect head · KITTI val, CarFPS: 17.4 -> 19.1Helped
Use accelerated PointNet aggregation instead of original PointNet (ablation)Voxel R-CNN detect head · KITTI val, CarFPS: 17.4 -> 21.4Helped
Combine detect head, voxel query, and accelerated PointNet aggregation (ablation)Voxel R-CNN · KITTI val, CarFPS: 40.8 -> 25.2Helped
Use voxel-only two-stage refinement rather than point-based PV-RCNNVoxel R-CNN · KITTI test, Car, Moderate3D AP: 81.43 -> 81.62Helped
Use voxel-only two-stage refinement rather than point-based PV-RCNNVoxel R-CNN · Waymo validation, LEVEL_1 vehicle3D mAP: 70.30 -> 75.59Helped
Use voxel-only two-stage refinement rather than point-based PV-RCNNVoxel R-CNN · Waymo validation, LEVEL_1 vehicle, 50m-Inf3D mAP: +10.98% mAPHelped
Use 3D voxel feature volumes for RoI refinement rather than BEV features alone (ablation)Voxel R-CNN · KITTI val, Car3D AP Moderate: 77.99 -> 84.52Helped

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