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.

基于单目摄像机的三维语义场景完成研究，提出了 DepthSSC 方法，通过 ST-GF 模块和几何感知体素化相结合，动态调整体素分辨率，考虑三维空间的几何复杂性，以确保空间与深度信息的精确对齐，成功解决了以往方法中存在的空间错位和畸变问题。通过在 SemanticKITTI 数据集上的评估，DepthSSC 不仅表现出在捕捉复杂的三维结构细节方面的有效性，还取得了最先进的性能。我们相信 DepthSSC 为基于单目摄像机的三维语义场景完成研究提供了新的视角，并希望它能激发更多相关研究。