  The task of 3D semantic scene completion with monocular cameras is gaining
increasing attention in the field of autonomous driving. Its objective is to
predict the occupancy status of each voxel in the 3D scene from partial image
inputs. Despite the existence of numerous methods, many of them overlook the
issue of accurate alignment between spatial and depth information. To address
this, we propose DepthSSC, an advanced method for semantic scene completion
solely based on monocular cameras. DepthSSC combines the ST-GF (Spatial
Transformation Graph Fusion) module with geometric-aware voxelization, enabling
dynamic adjustment of voxel resolution and considering the geometric complexity
of 3D space to ensure precise alignment between spatial and depth information.
This approach successfully mitigates spatial misalignment and distortion issues
observed in prior methods. Through evaluation on the SemanticKITTI dataset,
DepthSSC not only demonstrates its effectiveness in capturing intricate 3D
structural details but also achieves state-of-the-art performance. We believe
DepthSSC provides a fresh perspective on monocular camera-based 3D semantic
scene completion research and anticipate it will inspire further related
studies.
