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.

使用密集 - 稀疏 - 密集的设计，采用几何先验和占据信息，从语义感知和占据感知种子体素中扩散语义，实现基于相机的语义场景完成 (SSC) 框架。在 SemanticKITTI 数据集上的实验结果表明，该方法优于现有的最先进方法。