Learning Memory-Guided Scene Graph Reasoning for Temporal 3D Semantic Scene Completion

3D Semantic Scene Completion (SSC) aims to infer scene geometry and semantics from partial observations, providing dense 3D scene understanding for autonomous driving. Existing completion methods do not consistently adapt their completion strategies to different observation conditions and make limited use of object-level occlusion relations to recover missing scene information. However, occlusion limits the available visual evidence, and simply aggregation multi-frame features does not explicitly model directed occlusion relations between objects. To address these challenges, we propose MemGraphSSC, a temporal SSC framework that combines observation-conditioned memory with occlusion-relation-guided refinement. We introduce two complementary components: (1) an Observation-Conditioned Dual Memory module that retrieves historical region features for history-supported voxels and learned class prototypes for observation-unsupported voxels, while preserving visual initialization for current-supported voxels; and (2) an Occlusion-Relation-Guided Refinement module that uses depth-consistent relations from precomputed scene graphs to construct voxel priors and approximate object supports, then refines features in occluded regions through directed messages between relation endpoints. Experimental results show that the proposed method achieves superior performance on SemanticKITTI and SSCBench-KITTI-360 datasets, outperforming existing state-of-the-art vision-based SSC methods.