https://github.com/sshaoshuai/PartA2-Net

From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network, TPAMI 2020
https://github.com/sshaoshuai/PartA2-Net

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From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network, TPAMI 2020

• Host: GitHub
• URL: https://github.com/sshaoshuai/PartA2-Net
• Owner: sshaoshuai
• License: apache-2.0
• Created: 2020-02-19T10:23:59.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-03-16T13:17:28.000Z (over 4 years ago)
• Last Synced: 2024-01-16T05:32:02.090Z (5 months ago)
• Homepage:
• Size: 12.7 KB
• Stars: 93
• Watchers: 13
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-Autonomous-Driving - PartA2-Net - From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. (3. Perception / 3.1.2 Lidar based)
• awesome-point-cloud-papers-code-latest - [Arxiv - aware and Part-aggregation Network [[Code](https://github.com/open-mmlab/OpenPCDet)] (Paper list)

README

        
# From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

This repository is for our TPAMI paper [[arXiv]](https://arxiv.org/abs/1907.03670).

Code is available at [[PCDet]](https://github.com/sshaoshuai/PCDet).

**Authors**: [Shaoshuai Shi](https://sshaoshuai.github.io/), [Zhe Wang](https://wang-zhe.me/), [Jianping Shi](http://shijianping.me/), [Xiaogang Wang](http://www.ee.cuhk.edu.hk/~xgwang/), [Hongsheng Li](http://www.ee.cuhk.edu.hk/~hsli/).

## Introduction

In this work, we propose the part-aware and aggregation neural network (PartA2-Net). The whole framework consists of the part-aware stage and the part-aggregation stage. Firstly, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. 

At the time of submission (July-9 2019), our PartA2-Net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection [learderbaord](http://www.cvlibs.net/datasets/kitti/backups/2019_10_08_10_46_41_object_3d.html) by utilizing only the LiDAR point cloud data.

![teaser](https://sshaoshuai.github.io/content/images/PartA2_v2.png)

## Code

The code of `Part-A^2` has been released on [PCDet](https://github.com/sshaoshuai/PCDet).

## Citation

If you find this work useful in your research, please consider cite:

```

@article{shi2019part,

    title={From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network},

    author={Shi, Shaoshuai and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},

    journal={arXiv preprint arXiv:1907.03670},

    year={2019}

}

```