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[CVPR 2022 Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
| Date | Stars |
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| 2026-07-24 | 1177 |
| 2026-07-25 | 1177 |
| 2026-07-28 | 1177 |
| 2026-07-30 | 1177 |
| 2026-08-06 | 1177 |
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# EPro-PnP
📢 **NEWS:** We have released [EPro-PnP-v2](https://github.com/tjiiv-cprg/EPro-PnP-v2). A new updated preprint can be found on [arXiv](https://arxiv.org/abs/2303.12787).
**EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation**
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In CVPR 2022 (Oral, **Best Student Paper**). [[paper](https://arxiv.org/pdf/2203.13254.pdf)][[video](https://www.youtube.com/watch?v=TonBodQ6EUU)]
<br>
[Hansheng Chen](https://lakonik.github.io/)\*<sup>1,2</sup>, [Pichao Wang](https://wangpichao.github.io/)†<sup>2</sup>, [Fan Wang](https://scholar.google.com/citations?user=WCRGTHsAAAAJ&hl=en)<sup>2</sup>, [Wei Tian](https://scholar.google.com/citations?user=aYKQn88AAAAJ&hl=en)†<sup>1</sup>, [Lu Xiong](https://www.researchgate.net/scientific-contributions/Lu-Xiong-71708073)<sup>1</sup>, [Hao Li](https://scholar.google.com/citations?user=pHN-QIwAAAAJ&hl=zh-CN)<sup>2</sup>
<sup>1</sup>Tongji University, <sup>2</sup>Alibaba Group
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\*Part of work done during an internship at Alibaba Group.
<br>
†Corresponding Authors: Pichao Wang, Wei Tian.
## Introduction
EPro-PnP is a probabilistic Perspective-n-Points (PnP) layer for end-to-end 6DoF pose estimation networks. Broadly speaking, it is essentially a continuous counterpart of the widely used categorical Softmax layer, and is theoretically generalizable to other learning models with nested <!-- $\mathrm{arg\,min}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?%5Cmathrm%7Barg%5C%2Cmin%7D"> optimization.
<img src="intro.png" width="500" alt=""/>
Given the layer input: an <!-- $N$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?N">-point correspondence set <!-- $X = \left\{x^\text{3D}_i,x^\text{2D}_i,w^\text{2D}_i\,\middle|\,i=1\cdots N\right\}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?X%20%3D%20%5Cleft%5C%7Bx%5E%5Ctext%7B3D%7D_i%2Cx%5E%5Ctext%7B2D%7D_i%2Cw%5E%5Ctext%7B2D%7D_i%5C%2C%5Cmiddle%7C%5C%2Ci%3D1%5Ccdots%20N%5Cright%5C%7D"> consisting of 3D object coordinates <!-- $x^\text{3D}_i \in \mathbb{R}^3$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?x%5E%5Ctext%7B3D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E3">, 2D image coordinates <!-- $x^\text{2D}_i \in \mathbb{R}^2$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?x%5E%5Ctext%7B2D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E2">, and 2D weights <!-- $w^\text{2D}_i \in \mathbb{R}^2_+ $ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?w%5E%5Ctext%7B2D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E2_%2B">, a conventional PnP solver searches for an optimal pose <!-- $y^\ast$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?y%5E%5Cast"> (rigid transformation in SE(3)) that minimizes the weighted reprojection error. Previous work tries to backpropagate through the PnP operation, yet <!-- $y^\ast$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?y%5E%5Cast"> is inherently non-differentiable due to the inner <!-- $\mathrm{arg\,min}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?%5Cmathrm%7Barg%5C%2Cmin%7D"> operation. This leads to convergence issue if all the components in <!-- $X$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?X"> must be learned by the network.
In contrast, our probabilistic PnP layer outputs a posterior distribution of pose, whose probability density <!-- $p(y|X)$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?p(y%7CX)"> can be derived for pExcerpt of 8,801 characters
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matched fp:5e2fee5b9d1fe2c9, topic:pose-estimation, desc:pose estimation, readme:pose estimation
matched fp:5e2fee5b9d1fe2c9, topic:pytorch