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SOTA
3D Object Detection
3D Object Detection On Kitti Cars Easy
3D Object Detection On Kitti Cars Easy
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
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%
-
-
M3DeTR
90.28%
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
0 of 26 row(s) selected.
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