3D Object Detection On Kitti Cyclists

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AP	Paper Title	Repository
VoxelNet With Eloss	58%	Eloss in the way: A Sensitive Input Quality Metrics for Intelligent Driving
F-ConvNet	64.68%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
AVOD + Feature Pyramid	52.18%	Joint 3D Proposal Generation and Object Detection from View Aggregation
PointPillars	59.07%	PointPillars: Fast Encoders for Object Detection from Point Clouds
PointRCNN	59.60%	PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
3D-FCT	75.86%	3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation	-
SVGA-Net	66.13%	SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds	-
VoxelNet	48.36%	VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
PV-RCNN	63.71%	PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
M3DeTR	66.74%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
Frustum PointNets	56.77%	Frustum PointNets for 3D Object Detection from RGB-D Data
IPOD	53.46%	IPOD: Intensive Point-based Object Detector for Point Cloud	-
STD	62.53%	STD: Sparse-to-Dense 3D Object Detector for Point Cloud	-

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