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K
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SOTA
3D Object Detection
3D Object Detection On Kitti Cars Easy
3D Object Detection On Kitti Cars Easy
評価指標
AP
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
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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