Text-Guided Semantic Segmentation Method for Indoor 3D Point Clouds

Abstract. Point cloud semantic segmentation of indoor environments is a fundamental task in 3D scene understanding. However, existing methods mainly rely on geometric structures and color information, which are prone to error results in scenarios involving occlusion, sparse sampling, and geometrically similar structures. To address this issue, this paper proposes a text-knowledge-guided method for the point cloud semantic segmentation of indoor 3D scene. Built upon RandLA-Net as the baseline, the proposed method first constructs the textual semantic prototypes using multi-template prompts, and further enhances the stability of semantic anchors through periodic prototype refreshing. Then, a cross-modal semantic feature alignment mechanism is introduced at both the shallow and the high-level feature stages. Through feature alignment, bidirectional semantic interaction, and gated fusion, textual priors are progressively injected into the point cloud feature learning process. Finally, the model is jointly trained with a point-wise classification loss, a text-prototype alignment constraint, and a boundary optimization constraint to improve the semantic feature discrimination and segmentation boundary quality. Experimental results on the S3DIS dataset demonstrate that the proposed method achieves 86.8% OA, 81.6% mAcc, and 67.2% mIoU, exhibiting more stable segmentation performance in complex indoor scenes. As a consequence, these results indicate that incorporating textual semantic priors can effectively enhance high-level semantic representations of point clouds, providing a feasible solution for indoor 3D scene understand.