  Vision-centric 3D environment understanding is both vital and challenging for
autonomous driving systems. Recently, object-free methods have attracted
considerable attention. Such methods perceive the world by predicting the
semantics of discrete voxel grids but fail to construct continuous and accurate
obstacle surfaces. To this end, in this paper, we propose SurroundSDF to
implicitly predict the signed distance field (SDF) and semantic field for the
continuous perception from surround images. Specifically, we introduce a
query-based approach and utilize SDF constrained by the Eikonal formulation to
accurately describe the surfaces of obstacles. Furthermore, considering the
absence of precise SDF ground truth, we propose a novel weakly supervised
paradigm for SDF, referred to as the Sandwich Eikonal formulation, which
emphasizes applying correct and dense constraints on both sides of the surface,
thereby enhancing the perceptual accuracy of the surface. Experiments suggest
that our method achieves SOTA for both occupancy prediction and 3D scene
reconstruction tasks on the nuScenes dataset.
