OneBEV++: Towards Unifying Bird's-Eye-View Semantic Mapping with Panoramas.

Abstract

Bird's-Eye-View (BEV) perception, crucial for holistic scene understanding, faces limitations with narrow field-of-view sensors (indoors) and complex multi-camera setups (outdoors). To address these limitations and enable omni-range perception with a single image, we propose a new pipeline called panoramic-to-BEV semantic mapping. We extend existing household-related and driving-scene datasets to create four benchmarks for Panoramic BEV in both indoor and outdoor settings. As ground-breaking solutions, the 360BEV (indoor) and OneBEV (outdoor) methods have shown promise in their respective domains. Moreover, we propose a new framework OneBEV++, generating BEV semantic maps from a single panoramic image across indoor and outdoor environments. Extensive experiments on our four panoramic datasets demonstrate that OneBEV++ outperforms previous methods with consistent accuracy gains and improved efficiency. By simplifying complexity while enhancing performance, OneBEV++ enables scalable applications across diverse environments from outdoor autonomous vehicles to indoor robots, paving the way for more holistic cross-environment autonomous systems.