円筒ボクセルに基づく全方位深度支援占有予測
Omnidirectional Depth-Aided Occupancy Prediction based on Cylindrical Voxel for Autonomous Driving
全方位深度推定を幾何学的事前情報として活用し、極座標に基づく円筒ボクセル表現で自動運転向けの3D占有予測を行う手法を提案。魚眼カメラ6台の仮想データセットも構築した。
著者: Chaofan Wu, Jiaheng Li, Jinghao Cao, Ming Li, Yongkang Feng, Jiayu Wu Shuwen Xu, Zihang Gao, Sidan Du, Yang Li
分類: cs.CV, cs.RO
原文アブストラクト
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 OmniDepth-Occ. 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, and the data volume has reached twice that of SemanticKITTI. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.
