3D Object Detection On Kitti Pedestrians Easy

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
IPOD	56.92%	IPOD: Intensive Point-based Object Detector for Point Cloud
SVGA-Net	55.21%	SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
STD	53.08%	STD: Sparse-to-Dense 3D Object Detector for Point Cloud
F-ConvNet	52.37%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Frustrum-PointPillars	51.22 %	Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
Frustum PointNets	51.21%	Frustum PointNets for 3D Object Detection from RGB-D Data
AVOD + Feature Pyramid	50.8%	Joint 3D Proposal Generation and Object Detection from View Aggregation
M3DeTR	47.05%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
VoxelNet	39.48%	VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

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