Camera-based 3D Semantic Scene Completion with Sparse Guidance Network

Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving. Recently, many studies have turned to camera-based SSC solutions due to the richer visual cues and cost-effectiveness of cameras. However, existing methods usually rely on sophisticated and heavy 3D models to directly process the lifted 3D features that are not discriminative enough for clear segmentation boundaries. In this paper, we adopt the dense-sparse-dense design and propose an end-to-end camera-based SSC framework, termed SGN, to diffuse semantics from the semantic- and occupancy-aware seed voxels to the whole scene based on geometry prior and occupancy information. By designing hybrid guidance (sparse semantic and geometry guidance) and effective voxel aggregation for spatial occupancy and geometry priors, we enhance the feature separation between different categories and expedite the convergence of semantic diffusion. Extensive experimental results on the SemanticKITTI dataset demonstrate the superiority of our SGN over existing state-of-the-art methods.