Ming, Zhenxing (The University of Sydney), Berrio Perez, Julie Stephany (University of Sydney), Shan, Mao (University of Sydney), Huang, Yaoqi (The University of Sydney), Lyu, Hongyu (The University of Sydney), Tran, Nguyen Hoang Khoi (The University of Sydney), tseng, tzu-yun (University of Sydney, Australian Centre for Robotics), Worrall, Stewart (University of Sydney)

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

Scheduled for presentation during the Regular Session "S28b-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-EA-T28), Thursday, November 20, 2025, 14:50−14:50, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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. The code will be available at: https://github.com/DanielMing123/OccCylindrical