InSeg: Interactive Refinement via Intent Propagation for Point Cloud Semantic Segmentation

Despite recent progress in point cloud semantic segmenta-tion, obtaining deployment-grade predictions on individual scenes re-mains challenging. Interactive refinement offers a practical solution, yetexisting methods commonly interpret user clicks as single-point super-vision, leading to fragmented updates and requiring redundant user in-teraction. We identify this limitation as the Local Correction Trap. Ourkey insight is that a correction click more naturally expresses an in-tent to revise a semantically coherent region than to relabel an isolatedpoint. Based on this view, we propose InSeg, an interactive refinementframework that translates sparse clicks into reliable region-guided virtualsupervision. InSeg propagates corrective intent through feature affinity,spatial connectivity, and object-level priors to construct denoised virtuallabels. To support stable multi-round refinement, we further introducea history-aware mechanism that combines uncertainty-guided updateswith memory replay, reducing error accumulation and preventing previ-ously corrected regions from being overwritten. Extensive experimentsacross multiple benchmarks show that InSeg consistently improves seg-mentation quality and interaction efficiency, achieving strong gains withonly a few clicks.