OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping

• URL: http://arxiv.org/abs/2409.13912v1
• Date: Fri, 20 Sep 2024 21:33:53 GMT
• Title: OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
• Authors: Jiale Wei, Junwei Zheng, Ruiping Liu, Jie Hu, Jiaming Zhang, Rainer Stiefelhagen,
• Abstract summary: OneBEV is a novel BEV semantic mapping approach using merely a single panoramic image as input. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems.
• Score: 25.801868221496473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods, which rely on multiple narrow-field cameras and complex pose estimations, often face calibration and synchronization issues. To break the wall of the aforementioned challenges, in this work, we introduce OneBEV, a novel BEV semantic mapping approach using merely a single panoramic image as input, simplifying the mapping process and reducing computational complexities. A distortion-aware module termed Mamba View Transformation (MVT) is specifically designed to handle the spatial distortions in panoramas, transforming front-view features into BEV features without leveraging traditional attention mechanisms. Apart from the efficient framework, we contribute two datasets, i.e., nuScenes-360 and DeepAccident-360, tailored for the OneBEV task. Experimental results showcase that OneBEV achieves state-of-the-art performance with 51.1% and 36.1% mIoU on nuScenes-360 and DeepAccident-360, respectively. This work advances BEV semantic mapping in autonomous driving, paving the way for more advanced and reliable autonomous systems.

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