Unveiling the Significance of Width Dimension in Bird’s-Eye View Segmentation

Recent advancements in autonomous driving have prominently utilized the bird’s-eye view (BEV) as an intermediary representation of the world. However, with escalating precision requirements, the dimensionality of the BEV representation has expanded, leading to increased complexity. In this paper, we propose a novel real-time BEV segmentation model, which can perform height dimension reduction while view transformation. It also utilizes a novel decoder structure aimed at improving the fusion of overall situations and details. Consequently, we prove that the height dimension for BEV feature extraction and processing tasks is not a necessary component. Experimental results on the nuScenes dataset substantiate that our model surpasses previous approaches in terms of speed and achieves the state-of-the-art performance.