OV-RASF: Open-Vocabulary 3D Instance Segmentation via Reliability-Aware Semantic Fields

Open-Vocabulary 3D instance segmentation aims to localize objects and generate 3D instance masks from point clouds according to text queries. Existing methods struggle to balance efficiency and reliability: they either rely on foundation models for per-frame segmentation and classification, incurring high inference costs, or adopt bounding-box and multi-view label voting, which still suffers from in-box background interference, sparse semantic observations, and cross-view prediction inconsistency. The key issue is the lack of explicit modeling of instance-level semantic reliability. To this end, we propose OV-RASF, which, for the first time, introduces instance-level semantic reliability modeling into this task. The method projects and fuses multi-view vision-language features into 3D point space to construct an instance-queryable discrete 3D semantic field, and jointly quantifies reliability via semantic coverage and class response margin to adaptively adjust fusion weights. It leverages superpoint local consistency to constrain semantic propagation, completing masks and suppressing cross-instance leakage. Through class-residual gated fusion, it adaptively selects a more reliable prediction source for each class. The method requires no additional training and achieves more reliable 3D instance segmentation while exploiting the Open-Vocabulary recognition capability of 2D detectors. To validate the effectiveness of our method, we conduct experiments on two mainstream datasets (ScanNet200 and Replica). Results show that, compared with the current state-of-the-art methods, our method improves by 1.4% on ScanNet200 and 1.1% on Replica. The code will be made public later.