HyperAI
الرئيسية
الأخبار
أحدث الأوراق البحثية
الدروس
مجموعات البيانات
الموسوعة
SOTA
نماذج LLM
لوحة الأداء GPU
الفعاليات
البحث
حول
العربية
النظام
HyperAI
Toggle sidebar
البحث في الموقع...
⌘
K
تسجيل الدخول
تسجيل الدخول
الرئيسية
SOTA
3D Object Detection
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
Repository
HotSpotNet
44.81%
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
-
SVGA-Net
47.71%
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
-
IPOD
44.68%
IPOD: Intensive Point-based Object Detector for Point Cloud
-
Frustum PointNets
42.15%
Frustum PointNets for 3D Object Detection from RGB-D Data
3D-FCT
58.4%
3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation
-
STD
44.24%
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
-
M3DeTR
41.02%
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers
AVOD + Feature Pyramid
42.81%
Joint 3D Proposal Generation and Object Detection from View Aggregation
VoxelNet
33.69%
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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
PointPillars
41.92%
PointPillars: Fast Encoders for Object Detection from Point Clouds
0 of 12 row(s) selected.
Previous
Next