Papers › EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular...
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
Hansheng Chen, Wei Tian, Pichao Wang, Fan Wang, Lu Xiong, Hao Li
Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. Yet, learning the entire correspondences from scratch is highly challenging, particularly for ambiguous pose solutions, where the globally optimal pose is theoretically non-differentiable w.r.t. the points. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle generalizes previous approaches, and resembles the attention mechanism. EPro-PnP can enhance existing correspondence networks, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation benchmark. Furthermore, EPro-PnP helps to explore new possibilities of network design, as we demonstrate a novel deformable correspondence network with the state-of-the-art pose accuracy on the nuScenes 3D object detection benchmark. Our code is available at https://github.com/tjiiv-cprg/EPro-PnP-v2.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | NDS | 0.49 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mAAE | 0.123 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mAOE | 0.302 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mAP | 0.423 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mASE | 0.236 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mATE | 0.547 | #278 of 372 | Archive leaderboard | report |
| 3D Object Detection | nuScenes | EPro-PnP-Det v2 | mAVE | 1.071 | #278 of 372 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | EPro-PnP-6DoF v2 | Mean ADD | 96.36 | #4 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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