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OneBEV: Using One Panoramic Image for Bird's-Eye-View Semantic Mapping (ACCV 2024)
Introduction
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.
Updates
- 09/2024, init repository, code and datasets are coming soon...