EPro-PnP

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
In CVPR 2022 (Oral). [paper]
Hansheng Chen*^1,2, Pichao Wang†², Fan Wang², Wei Tian†¹, Lu Xiong¹, Hao Li²

¹Tongji University, ²Alibaba Group
*Part of work done during an internship at Alibaba Group.
†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 optimization.

Given the layer input: an -point correspondence set consisting of 3D object coordinates , 2D image coordinates , and 2D weights , a conventional PnP solver searches for an optimal pose (rigid transformation in SE(3)) that minimizes the weighted reprojection error. Previous work tries to backpropagate through the PnP operation, yet is inherently non-differentiable due to the inner operation. This leads to convergence issue if all the components in must be learned by the network.

In contrast, our probabilistic PnP layer outputs a posterior distribution of pose, whose probability density can be derived for proper backpropagation. The distribution is approximated via Monte Carlo sampling. With EPro-PnP, the correspondences can be learned from scratch altogether by minimizing the KL divergence between the predicted and target pose distribution.

Models

We release two distinct networks trained with EPro-PnP:

• EPro-PnP-6DoF for 6DoF pose estimation
  

• EPro-PnP-Det for 3D object detection

  

Use EPro-PnP in Your Own Model

We provide a demo on the usage of the EPro-PnP layer.

Citation

If you find this project useful in your research, please consider citing:

@inproceedings{epropnp, 
  author = {Hansheng Chen and Pichao Wang and Fan Wang and Wei Tian and Lu Xiong and Hao Li, 
  title = {EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation}, 
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  year = {2022}
}