NEWS: An updated journal version of this paper has been released here. The new code will be made available soon.

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
In CVPR 2022 (Oral, Best Student Paper). [paper][video]
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.

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 used in the original paper:

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

• EPro-PnP-Det for 3D object detection

  

New models:

• EPro-PnP-Det v2 for 3D object detection

  At the time of submission (Aug 30, 2022), EPro-PnP-Det v2 ranks 1st among all camera-based single-frame object detection models on the official nuScenes benchmark (test split, without extra data). The new paper has been released here.

  Method	Backbone	NDS	mAP	mATE	mASE	mAOE	mAVE	mAAE	Schedule
  EPro-PnP-Det v2	R101	0.490	0.423	0.547	0.236	0.302	1.071	0.123	12 ep
  PETR	Swin-B	0.483	0.445	0.627	0.249	0.449	0.927	0.141	24 ep
  BEVDet-Base	Swin-B	0.482	0.422	0.529	0.236	0.395	0.979	0.152	20 ep
  PolarFormer	R101	0.470	0.415	0.657	0.263	0.405	0.911	0.139	24 ep
  BEVFormer-S	R101	0.462	0.409	0.650	0.261	0.439	0.925	0.147	24 ep
  PETR	R101	0.455	0.391	0.647	0.251	0.433	0.933	0.143	24 ep
  EPro-PnP-Det v1	R101	0.453	0.373	0.605	0.243	0.359	1.067	0.124	12 ep
  PGD	R101	0.448	0.386	0.626	0.245	0.451	1.509	0.127	24+24 ep
  FCOS3D	R101	0.428	0.358	0.690	0.249	0.452	1.434	0.124	-

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

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}
}