NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation

Interactive 3D segmentation aims to extract object masks inpoint clouds with minimal user clicks. Despite recent progress, most ex-isting approaches still struggle with (i) coarse voxel resolution that blursfine boundaries under limited clicks and (ii) hard false positives causedby confusing background structures. These issues are exacerbated bydensity and scale shifts across datasets (e.g., dense RGB-D reconstruc-tions vs. sparse LiDAR scans), where fixed refinement heuristics andpurely click-driven decoding generalize poorly. To address them, we pro-pose NegROI — a novel transformer-based interactive framework thatcouples click-centric multi-resolution refinement with scene-conditionednegative prompts. Given a coarse voxel prediction, it refines only a lo-cal Region Of Interest (ROI) around the current click on a finer gridand fuses refined logits back to the coarse mask. To improve robustnessand efficiency, we introduce uncertainty-driven selective refinement thatprioritizes ambiguous regions. Meanwhile, we model hard backgroundpatterns via a set of scene-conditioned negative prompts obtained bycross-attention over scene tokens. We further stabilize these prompts witha diversity regularizer. Finally, we propose boundary-aware hard nega-tive mining to supervise negative-prompt attention toward boundary-proximal, high-confidence false positives. Our experiments on commonbenchmark datasets (i.e., ScanNet, S3DIS, and KITTI) demonstrate im-proved click efficiency and reduced false positives, with stronger cross-dataset robustness than the state-of-the-art baselines.