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https://github.com/tjiiv-cprg/EPro-PnP

[CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
https://github.com/tjiiv-cprg/EPro-PnP

3d-object-detection 6dof cvpr gauss-newton levenberg-marquardt monocular perspective-n-point pose-estimation pytorch

Last synced: about 2 months ago
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[CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Host: GitHub
URL: https://github.com/tjiiv-cprg/EPro-PnP
Owner: tjiiv-cprg
License: apache-2.0
Created: 2022-03-20T11:48:33.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-07-30T15:29:07.000Z (about 1 year ago)
Last Synced: 2024-06-08T11:34:19.472Z (3 months ago)
Topics: 3d-object-detection, 6dof, cvpr, gauss-newton, levenberg-marquardt, monocular, perspective-n-point, pose-estimation, pytorch
Language: Python
Homepage: https://www.youtube.com/watch?v=TonBodQ6EUU
Size: 12.4 MB
Stars: 1,062
Watchers: 14
Forks: 102
Open Issues: 50
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # 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**




In CVPR 2022 (Oral, **Best Student Paper**). [[paper](https://arxiv.org/pdf/2203.13254.pdf)][[video](https://www.youtube.com/watch?v=TonBodQ6EUU)]




[Hansheng Chen](https://lakonik.github.io/)\*^1,2, [Pichao Wang](https://wangpichao.github.io/)†², [Fan Wang](https://scholar.google.com/citations?user=WCRGTHsAAAAJ&hl=en)², [Wei Tian](https://scholar.google.com/citations?user=aYKQn88AAAAJ&hl=en)†¹, [Lu Xiong](https://www.researchgate.net/scientific-contributions/Lu-Xiong-71708073)¹, [Hao Li](https://scholar.google.com/citations?user=pHN-QIwAAAAJ&hl=zh-CN)²

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

### V1 models in this repository

#### **[EPro-PnP-6DoF](EPro-PnP-6DoF) for 6DoF pose estimation**


  

#### **[EPro-PnP-Det](EPro-PnP-Det) for 3D object detection**

  

### New V2 models

#### **[EPro-PnP-Det v2](https://github.com/tjiiv-cprg/EPro-PnP-v2/tree/main/EPro-PnP-Det_v2): state-of-the-art monocular 3D object detector**

Main differences to [v1b](EPro-PnP-Det):

- 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](https://www.nuscenes.org/object-detection?externalData=no&mapData=no&modalities=Camera) (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](https://github.com/megvii-research/petr)          |  N  | Swin-B   |   0.483   | **0.445** |   0.627   |   0.249   |   0.449   |   0.927   |   0.141   |  24 ep   |

| [BEVDet-Base](https://github.com/HuangJunJie2017/BEVDet) |  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](https://github.com/fudan-zvg/PolarFormer)  |  N  | R101     |   0.470   |   0.415   |   0.657   |   0.263   |   0.405   | **0.911** |   0.139   |  24 ep   |

| [BEVFormer-S](https://github.com/zhiqi-li/BEVFormer)     |  N  | R101     |   0.462   |   0.409   |   0.650   |   0.261   |   0.439   |   0.925   |   0.147   |  24 ep   |

| [PETR](https://github.com/megvii-research/petr)          |  N  | R101     |   0.455   |   0.391   |   0.647   |   0.251   |   0.433   |   0.933   |   0.143   |  24 ep   |

| [EPro-PnP-Det v1](EPro-PnP-Det_v2)                       |  Y  | R101     |   0.453   |   0.373   |   0.605   |   0.243   |   0.359   |   1.067   |   0.124   |  12 ep   | 

| [PGD](https://github.com/open-mmlab/mmdetection3d)       |  Y  | R101     |   0.448   |   0.386   |   0.626   |   0.245   |   0.451   |   1.509   |   0.127   | 24+24 ep |

| [FCOS3D](https://github.com/open-mmlab/mmdetection3d)    |  Y  | R101     |   0.428   |   0.358   |   0.690   |   0.249   |   0.452   |   1.434   |   0.124   |    -     |

#### **[EPro-PnP-6DoF v2](https://github.com/tjiiv-cprg/EPro-PnP-v2/tree/main/EPro-PnP-6DoF_v2) for 6DoF pose estimation**


Main differences to [v1b](EPro-PnP-6DoF):

- Fix 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](https://github.com/THU-DA-6D-Pose-Group/GDR-Net) (ADD 0.1d = 93.6), unleashing the full potential of simple end-to-end training.

## Use EPro-PnP in Your Own Model

We provide a [demo](demo/fit_identity.ipynb) 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}

}

```