3D Object Detection On Kitti Cars Hard

Metrics

Results

Performance results of various models on this benchmark

Model Name	AP	Paper Title	Repository
SA-SSD+EBM	72.78%	Accurate 3D Object Detection using Energy-Based Models	-
Joint	74.30%	Joint 3D Instance Segmentation and Object Detection for Autonomous Driving	-
F-ConvNet	68.08%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection	-
3D Dual-Fusion	79.39%	3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection	-
PC-RGNN	75.54%	PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection	-
SVGA-Net	74.63%	SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds	-
VoxelNet	57.73%	VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection	-
TRTConv	80.38 %	-	-
PV-RCNN++	77.15%	PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection	-
UberATG-MMF	68.41%	Multi-Task Multi-Sensor Fusion for 3D Object Detection	-
PC-CNN-V2	64.83%	A General Pipeline for 3D Detection of Vehicles	-
GLENet-VR	78.43%	GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation	-
M3DeTR	76.96%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers	-
Voxel R-CNN	77.06	Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection	-
PGD	9.39%	Probabilistic and Geometric Depth: Detecting Objects in Perspective	-
AVOD + Feature Pyramid	66.38%	Joint 3D Proposal Generation and Object Detection from View Aggregation	-
IPOD	66.33%	IPOD: Intensive Point-based Object Detector for Point Cloud	-
Frustum PointNets	62.19%	Frustum PointNets for 3D Object Detection from RGB-D Data	-
PV-RCNN	76.82%	PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection	-
STD	76.06%	STD: Sparse-to-Dense 3D Object Detector for Point Cloud	-

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