Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation

arXiv:2308.14525v2 Announce Type: replace
Abstract: Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capability of the visual BEV semantic segmentation, which usually needs HD maps, 3D object bounding boxes, and camera extrinsic matrixes. In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to …

arxiv bird cs.cv segmentation semantic semi-supervised semi-supervised learning supervised learning type view visual