OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping
This addresses calibration and synchronization issues in autonomous driving perception by simplifying the mapping process, though it appears incremental as it builds on existing BEV methods with a new input type.
The paper tackles BEV semantic mapping for autonomous driving by introducing OneBEV, which uses only a single panoramic image as input instead of multiple cameras, achieving state-of-the-art performance with 51.1% mIoU on nuScenes-360 and 36.1% mIoU on DeepAccident-360.
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.