3D-Occupancy-Perception

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3D-Occupancy-Perception

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[Information Fusion 2025] A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective

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<a href="https://arxiv.org/abs/2405.05173">
<img width="765" alt="image" src="assets/title.png">
 </a>
 
 <a href="https://scholar.google.com.hk/citations?user=kpMGaNIAAAAJ&hl=zh-CN">Huaiyuan Xu </a>
 .
 <a href="https://scholar.google.com/citations?user=kqU2NJIAAAAJ&hl=zh-CN">Junliang Chen </a>
 .
 Shiyu Meng</a>
 .
 <a href="https://scholar.google.com/citations?user=MAG909MAAAAJ&hl=en">Yi Wang</a>
 .
 <a href="https://scholar.google.com/citations?user=MYREIH0AAAAJ&hl=zh-CN">Lap-Pui Chau*</a>
 

 

 <a href='https://arxiv.org/abs/2405.05173'>
 <img src='https://img.shields.io/badge/arXiv-PDF-green?style=flat&logo=arXiv&logoColor=green' alt='arXiv PDF'>
 </a>

# We research 3D Occupancy Perception for Autonomous Driving

This work focuses on 3D dense perception in autonomous driving, encompassing **LiDAR-Centric Occupancy Perception**, **Vision-Centric Occupancy Perception**, and **Multi-Modal Occupancy Perception**. Information fusion techniques for this field are discussed. We believe this will be the most comprehensive survey to date on 3D Occupancy Perception. Please stay tuned!😉😉😉

This is an active repository, you can watch for following the latest advances. If you find it useful, please kindly star this repo.

### ✨You are welcome to provide us your work with a topic related to 3D occupancy for autonomous driving (involving not only perception, but also applications)!

If you discover any missing work or have any suggestions, please feel free to submit a [pull request](https://github.com/HuaiyuanXu/3D-Occupancy-Perception/issues) or [contact us](#contact). We will promptly add the missing papers to this repository.

###  ✨Highlight

[1]  A systematically survey for the latest research on 3D occupancy
perception in the field of autonomous driving.

[2]  The survey provides the taxonomy of 3D occupancy perception,
and elaborate on core methodological issues, including
network pipelines, multi-source information fusion, and
effective network training.

[3]  The survey presents evaluations for 3D occupancy perception, and
offers detailed performance comparisons. Furthermore,
current limitations and future research directions are discussed.

###  🔥 News
- [2024-09-03]  This survey got accepted by Information Fusion (Impact factor: 14.7).
- [2024-07-21]  More representative works and benchmarking comparisons have been incorporated, bringing the total to 192 literature references.
- [2024-05-18]  More figures have been added to the survey. We reorganize the occupancy-based applications.
- [2024-05-08] The first version of the survey is available on [arXiv](https://arxiv.org/abs/2405.05173). We curate this repository.

## Introduction

3D occupancy perception technology aims to observe and understand dense 3D environments for autonomous vehicles. Owing to its comprehensive perception capability, this technology is emerging as a trend in autonomous driving perception systems, and is attracting significant attention from both industry and academia. Similar to traditional bird's-eye view (BEV) perception, 3D occupancy perception has the nature of multi-source input and the necessity for information fusion. However, the difference is that it captures vertical structures that are ignored by 2D BEV. In this survey, we review the most recent works on 3D occupancy perception, and provide in-depth analyses of methodologies with various input modalities. Specifically, we summarize general network pipelines, highlight information fusion techniques, and discuss effective network training. We evaluate and analyze the occupancy perception performance of the state-of-the-art on the most popular datasets. Furthermore, challenges and future research directions are discussed. We hope this paper will inspire the community and encourage more research work on 3D occupancy perception.

## Summary of Contents
- [Introduction](#introduction)
- [Summary of Contents](#summary-of-contents)
- [Methods: A Survey](#methods-a-survey)
  - [LiDAR-Centric Occupancy Perception](#liDAR-centric-occupancy-perception)
  - [Vision-Centric Occupancy Perception](#vision-centric-occupancy-perception)
  - [Radar-Centric Occupancy Perception](#radar-centric-occupancy-perception)
  - [Multi-Modal Occupancy Perception](#multi-modal-occupancy-perception)
- [3D Occupancy Datasets](#3D-Occupancy-Datasets)
- [Occupancy-based Applications](#occupancy-based-applications)
- [Cite The Survey](#Cite-The-Survey)
- [Contact](#contact)

## Methods: A Survey

### Radar-Centric Occupancy Perception
|Year|Venue|Paper Title|Link|
|:-:|:-:|-|-|
|2025|arXiv|[4D-ROLLS: 4D Radar Occupancy Learning via LiDAR Supervision](https://arxiv.org/abs/2505.13905)|[Code](https://github.com/CLASS-Lab/4D-ROLLS)|
|2024|NeurIPS|[RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://arxiv.org/abs/2405.14014)|-|

## 3D Occupancy Datasets

## Cite The Survey
If you find our survey and repository useful for your research project, please consider citing our paper:

```bibtex
@misc{xu2024survey,
      title={A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective}, 
      author={Huaiyuan Xu and Junliang Chen and Shiyu Meng and Yi Wang and Lap-Pui Chau},
      year={2024},
      eprint={2405.05173},
      archivePrefix={arXiv}
}
```
## Contact
If you have any questions, please feel free to get in touch:
```
lap-pui.chau@polyu.edu.hk
huaiyuan.xu@polyu.edu.hk
```
If you are interested in joining us as a Ph.D. student to research **computer vision, machine learning**, please feel free to contact [Professor Chau](https://www.polyu.edu.hk/eee/people/academic-staff-and-teaching-staff/prof-chau-lap-pui/):
```
lap-pui.chau@polyu.edu.hk
```

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