OccCylindrical

OccCylindrical: Multi-Modal Fusion with Cylindrical Representation for 3D Semantic Occupancy Prediction [Paper]

Abstract

The safe operation of autonomous vehicles (AVs) is highly dependent on their understanding of the surroundings. For this, the task of 3D semantic occupancy prediction divides the space around the sensors into voxels, and labels each voxel with both occupancy and semantic information. Recent perception models have used multisensor fusion to perform this task. However, existing multisensor fusion-based approaches focus mainly on using sensor information in the Cartesian coordinate system. This ignores the distribution of the sensor readings, leading to a loss of fine-grained details and performance degradation. In this paper, we propose OccCylindrical that merges and refines the different modality features under cylindrical coordinates. Our method preserves more fine-grained geometry detail that leads to better performance. Extensive experiments conducted on the nuScenes dataset, including challenging rainy and nighttime scenarios, confirm our approach’s effectiveness and state-of-the-art performance.

Overall Architecture

Benchmark

SurroundOcc-Nuscenes val set

Model Zoo

Input Modality	IoU	mIoU	Model Weights
C+L	44.94	28.67	ckpt

Getting Started

• Installation
• Dataset
• Run and Eval

Acknowledgement

Our work is inspired by these excellent open-sourced repos, many thanks to these excellent projects:

• SurroundOcc
• Occ3D
• PointOcc

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@INPROCEEDINGS{11423837,
  author={Ming, Zhenxing and Berrio, Julie Stephany and Shan, Mao and Huang, Yaoqi and Lyu, Hongyu and Tran, Nguyen Hoang Khoi and Tseng, Tzu-Yun and Worrall, Stewart},
  booktitle={2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={Occcylindrical: Multi-Modal Fusion with Cylindrical Representation for 3D Semantic Occupancy Prediction}, 
  year={2025},
  volume={},
  number={},
  pages={1981-1988},
  keywords={Point cloud compression;Geometry;Three-dimensional displays;Laser radar;Semantics;Sensor systems;Robustness;Vehicle dynamics;Intelligent transportation systems;Intelligent sensors},
  doi={10.1109/ITSC60802.2025.11423837}}