A Point Cloud Instance Segmentation Framework with Attention Mechanisms and Semantic Refinement

INTRODUCTION: Point cloud instance segmentation, a critical 3D computer vision task, faces significant challenges in complex indoor environments. Methods often suffer from insufficient feature extraction and a strong coupling between semantic prediction and instance features, where semantic errors cascade and limit overall accuracy. OBJECTIVES: This paper proposes an innovative approach using attention mechanisms and semantic refinement to address these limitations. The primary goal is to enhance feature representation and alleviate the strong dependency between semantic prediction and instance segmentation. METHODS: We introduce three key innovations: 1) A reverse attention mechanism to improve multi-level feature fusion; 2) An instance soft clustering strategy incorporating semantic scores to weaken the semantic-instance coupling; and 3) A self-attention-based instance refinement network. Finally, a dual-branch scoring mechanism, combining classification and mask scores, jointly determines confidence levels to further mitigate semantic errors. RESULTS: Evaluated on the S3DIS dataset, our model achieved 74.1% mean Precision(mPrec) on the Area 5 test. In the more rigorous six-fold cross-validation, it achieved 76.8% mPrec and 72.3% mean recall rate(mRec), outperforming the state-of-the-art (SOTA) model SoftGroup by 1.5% and 2.5%, respectively. CONCLUSION: The proposed method significantly improves instance segmentation accuracy and demonstrates stronger robustness in complex scenes. It effectively resolves the strong coupling issue, providing a novel technical pathway for point cloud instance segmentation. While primarily optimized for dense indoor scans, adapting this framework for extremely sparse outdoor point clouds remains a compelling direction for future exploration.