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EPro-PnP v2

This repository contains the upgraded code for the CVPR 2022 paper EPro-PnP, featuring improved models for both the 6DoF and 3D detection benchmarks.

A new updated preprint can be found on arXiv: EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation.

Models

EPro-PnP-Det v2: state-of-the-art monocular 3D object detector

Main differences to v1b:

Use GaussianMixtureNLLLoss as auxiliary coordinate regression loss
Add auxiliary depth and bbox losses

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).

Method	TTA	Backbone	NDS	mAP	mATE	mASE	mAOE	mAVE	mAAE	Schedule
EPro-PnP-Det v2 (ours)	Y	R101	0.490	0.423	0.547	0.236	0.302	1.071	0.123	12 ep
PETR	N	Swin-B	0.483	0.445	0.627	0.249	0.449	0.927	0.141	24 ep
BEVDet-Base	Y	Swin-B	0.482	0.422	0.529	0.236	0.395	0.979	0.152	20 ep
EPro-PnP-Det v2 (ours)	N	R101	0.481	0.409	0.559	0.239	0.325	1.090	0.115	12 ep
PolarFormer	N	R101	0.470	0.415	0.657	0.263	0.405	0.911	0.139	24 ep
BEVFormer-S	N	R101	0.462	0.409	0.650	0.261	0.439	0.925	0.147	24 ep
PETR	N	R101	0.455	0.391	0.647	0.251	0.433	0.933	0.143	24 ep
EPro-PnP-Det v1	Y	R101	0.453	0.373	0.605	0.243	0.359	1.067	0.124	12 ep
PGD	Y	R101	0.448	0.386	0.626	0.245	0.451	1.509	0.127	24+24 ep
FCOS3D	Y	R101	0.428	0.358	0.690	0.249	0.452	1.434	0.124	-

EPro-PnP-6DoF v2 for 6DoF pose estimation<br>

Main differences to v1b:

Improve w2d scale handling (very important)
Improve network initialization
Adjust loss weights

With these updates the v2 model can be trained without 3D models to achieve better performance (ADD 0.1d = 93.83) than GDRNet (ADD 0.1d = 93.6), unleashing the full potential of simple end-to-end training.

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}
}