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المنصة
الرئيسية
SOTA
كشف الأشياء ثلاثية الأبعاد
3D Object Detection On Kitti Pedestrians
3D Object Detection On Kitti Pedestrians
المقاييس
AP
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
AP
Paper Title
3D-FCT
58.4%
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation
SVGA-Net
47.71%
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
HotSpotNet
44.81%
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
IPOD
44.68%
IPOD: Intensive Point-based Object Detector for Point Cloud
STD
44.24%
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
F-ConvNet
43.38%
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection
Frustrum-PointPillars
42.89 %
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
AVOD + Feature Pyramid
42.81%
Joint 3D Proposal Generation and Object Detection from View Aggregation
Frustum PointNets
42.15%
Frustum PointNets for 3D Object Detection from RGB-D Data
PointPillars
41.92%
PointPillars: Fast Encoders for Object Detection from Point Clouds
M3DeTR
41.02%
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
VoxelNet
33.69%
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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3D Object Detection On Kitti Pedestrians | SOTA | HyperAI