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3D Multi Person Pose Estimation On Campus

Métriques

Mean mAP
PCP3D

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Mean mAP
PCP3D
Paper TitleRepository
VTP80.196.3VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation-
TesseTrack-97.4TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking-
PlaneSweepPose-97Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo-
Ershadi-Nasab model-90.6Multiple human 3d pose estimation from multiview images-
MvP-96.6Direct Multi-view Multi-person 3D Pose Estimation-
MVPose-96.3Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views-
Faster VoxelPose-96.9Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projection-
VoxelTrack-96.7VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild-
Belagiannis model v.3-84.53D Pictorial Structures Revisited: Multiple Human Pose Estimation-
Belagiannis model v.2-78Multiple human pose estimation with temporally consistent 3d pictorial structures-
SmartEdgeSensor-97Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors-
Part-aware Pose-96.79Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking-
VoxelPose-96.7VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment-
UMMT-97.0A Unified Multi-view Multi-person Tracking Framework-
Cross-View-96.6Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS-
crossview_3d_pose_tracking-96.6Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS-
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3D Multi Person Pose Estimation On Campus | SOTA | HyperAI