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The task of 3D semantic scene completion using monocular cameras is gainingsignificant attention in the field of autonomous driving. This task aims topredict the occupancy status and semantic labels of each voxel in a 3D scenefrom partial image inputs. Despite numerous existing methods, many facechallenges such as inaccurately predicting object shapes and misclassifyingobject boundaries. To address these issues, we propose DepthSSC, an advancedmethod for semantic scene completion using only monocular cameras. DepthSSCintegrates the Spatial Transformation Graph Fusion (ST-GF) module withGeometric-Aware Voxelization (GAV), enabling dynamic adjustment of voxelresolution to accommodate the geometric complexity of 3D space. This ensuresprecise alignment between spatial and depth information, effectively mitigatingissues such as object boundary distortion and incorrect depth perception foundin previous methods. Evaluations on the SemanticKITTI and SSCBench-KITTI-360dataset demonstrate that DepthSSC not only captures intricate 3D structuraldetails effectively but also achieves state-of-the-art performance.

DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation