Knowledge Vault 5 /77 - CVPR 2022
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, Hao Li
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef main fill:#f9f9f9, font-weight:bold, font-size:14px classDef pose fill:#d4f9f4, font-weight:bold, font-size:14px classDef loss fill:#f9d4d4, font-weight:bold, font-size:14px classDef network fill:#d4d4f9, font-weight:bold, font-size:14px classDef result fill:#f9f9d4, font-weight:bold, font-size:14px A[EPro-PnP: Generalized End-to-End
Probabilistic Perspective-n-Points for
Monocular Object Pose
Estimation] --> B[EPro-PnP: Probabilistic
Perspective-n-Points layer. 1] B --> C[Predicts pose distribution
to capture ambiguity. 2] B --> D[KL divergence loss
between distributions. 3] D --> E[Monte Carlo approach
estimates pose loss integral. 4] C --> F[Corresponding weights balance
uncertainty and attention. 5] B --> G[Pose density derivatives
regularized for optimization. 6] B --> H[Unifies and outperforms
prior PnP approaches. 7] B --> I[CDPN with EPro-PnP
improves performance. 8] I --> J[Deformable correspondence network
learns 2D-3D points. 9] B --> K[Outperforms state-of-the-art on
LineMOD and nuScenes benchmarks. 10] class A main class B,C,F,G pose class D,E loss class I,J network class H,K result


1.- EPro-PnP: Probabilistic Perspective-n-Points layer for end-to-end object pose estimation from 2D-3D point correspondences.

2.- Pose ambiguity captured by predicting pose distribution instead of deterministic pose.

3.- KL divergence between predicted and target pose distributions used as training loss.

4.- Monte Carlo approach efficiently estimates pose loss integral.

5.- Corresponding weights balance uncertainty and pose discrimination, resembling attention mechanism.

6.- Derivatives of pose density regularized to aid optimization.

7.- Unifies and outperforms prior PnP-based approaches.

8.- Dense correspondence network modified from CDPN with EPro-PnP significantly improves performance.

9.- Novel deformable correspondence network learns 2D-3D points from scratch for 3D object detection.

10.- Outperforms state-of-the-art 6DoF pose estimation and 3D object detection methods on LineMOD and nuScenes benchmarks.

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