Enhancing 3D semantic scene completion via efficient attention and feature augmentation

Abstract

3D Semantic Scene Completion (SSC), a cornerstone task in computer vision, aims to simultaneously predict the geometric structure and semantic content of 3D scenes from sparse inputs. This capability is crucial for advancing applications in robotics, autonomous driving, and virtual reality. However, SSC faces significant challenges, including the high computational cost of capturing long-range contextual information and the scarcity of 3D semantic labels leading to overfitting. To address these limitations, we propose an enhanced network for semantic scene completion. Firstly, we devise a 3D Local- Global Linear Attention Mechanism (LG-LAM) that efficiently captures long-range contextual information with linear complexity, enabling a comprehensive understanding of the 3D scene without heavy computational burdens. Secondly, a 3D Feature Augmentation Module (FAM) is integrated to enrich feature diversity through rotation-invariant learning, mitigating overfitting and enhancing the model’s robustness given limited annotations. Extensive experiments on the NYUCAD dataset demonstrate that our method achieves state-of-the-art performance among non-iterative methods while introducing negligible computational overhead.