3D Object Detection On Kitti Cars Easy

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Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	AP	Paper Title	Repository
TRTConv	91.90 %	-	-
SA-SSD+EBM	91.05%	Accurate 3D Object Detection using Energy-Based Models	-
PV-RCNN++	90.14%	PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection	-
RoarNet	83.71%	RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement	-
PC-RGNN	89.13%	PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection	-
STD	86.61%	STD: Sparse-to-Dense 3D Object Detector for Point Cloud	-
F-ConvNet	85.88%	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection	-
PGD	19.05%	Probabilistic and Geometric Depth: Detecting Objects in Perspective	-
3D Dual-Fusion	91.01%	3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection	-
GLENet-VR	91.67%	GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation	-
AVOD + Feature Pyramid	81.94%	Joint 3D Proposal Generation and Object Detection from View Aggregation	-
PointRCNN	84.32%	PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud	-
SVGA-Net	87.33%	SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds	-
Frustum PointNets	81.2%	Frustum PointNets for 3D Object Detection from RGB-D Data	-
PV-RCNN	90.25%	PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection	-
SE-SSD	91.49%	SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud	-
PC-CNN-V2	84.33%	A General Pipeline for 3D Detection of Vehicles	-
Joint	87.74%	Joint 3D Instance Segmentation and Object Detection for Autonomous Driving	-
PointRGCN	85.97%	PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement	-
M3DeTR	90.28%	M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers	-

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