GeoScene: Temporal 3D Semantic Scene Completion with Geometric Correlation between Images

Abstract

Semantic Scene Completion (SSC) aims to reconstruct the entire 3D scene in terms of both occupancy and semantics, serving as a fundamental task for autonomous driving and robotic systems. Camera-based methods have seen significant advancements due to their low cost and rich visual cues. However, previous approaches have predominantly focused on semantic recovery. This can lead to inaccurate occupancy predictions and, consequently, the failure of downstream tasks such as trajectory planning. To address this limitation, we propose a novel multi-frame matching framework, GeoScene, which reconstructs spatial structures through inter-frame geometric correlations of temporal images and subsequently infers scene semantic information. Specifically, we extract features from distinct frames in the depth dimension and derive depth features by constructing a cost volume. Following this, dot product and voxelization operations are applied between the extracted features and depth features to correct assignment errors. Furthermore, we introduce a surface normal-based regression loss to preserve fine-grained surface structures. Extensive experiments on the SemanticKITTI dataset demonstrate that GeoScene outperforms existing state-of-the-art methods.