OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems
Year: 2025
Type: article
Abstract: Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniOcc. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.
Source: IEEE Access
Authors: Chaofan Wu, Jiaheng Li, Jinghao Cao, Ming Li, Sidan Du, Yang Li
Institution: Nanjing University
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Award: G1309472000
OpenAlex ID: W4412984986