3D Object Detection On Kitti Cars Hard

评估指标

评测结果

各个模型在此基准测试上的表现结果

		Paper Title	Repository
TRTConv	80.38 %	-	-
3D Dual-Fusion	79.39%	3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
GLENet-VR	78.43%	GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
PV-RCNN++	77.15%	PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
SE-SSD	77.15%	SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
Voxel R-CNN	77.06	Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
M3DeTR	76.96%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
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	-
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	-
Joint	74.30%	Joint 3D Instance Segmentation and Object Detection for Autonomous Driving	-
CIA-SSD	72.87	CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
SA-SSD+EBM	72.78%	Accurate 3D Object Detection using Energy-Based Models
PointRGCN	70.60%	PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement	-
UberATG-MMF	68.41%	Multi-Task Multi-Sensor Fusion for 3D Object Detection	-
F-ConvNet	68.08%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
PointRCNN	67.86%	PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
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	-

0 of 25 row(s) selected.

Command Palette

3D Object Detection On Kitti Cars Hard

评估指标

评测结果