3D Object Detection On Kitti Pedestrians Hard

Results

Performance results of various models on this benchmark

		Paper Title
SVGA-Net	44.56%	SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
IPOD	42.39%	IPOD: Intensive Point-based Object Detector for Point Cloud
STD	41.97%	STD: Sparse-to-Dense 3D Object Detector for Point Cloud
F-ConvNet	41.49%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
AVOD + Feature Pyramid	40.88%	Joint 3D Proposal Generation and Object Detection from View Aggregation
Frustum PointNets	40.23%	Frustum PointNets for 3D Object Detection from RGB-D Data
Frustrum-PointPillars	39.28 %	Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
M3DeTR	38.75%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
VoxelNet	31.51%	VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

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