MSR-3D: Multi-Mode Semantic Representation for Open-Vocabulary 3D Scene Understanding

3DGS-based open-vocabulary 3D scene understanding requires identifying reconstructed 3D instances that correspond to a natural-language query. Existing methods typically compress multi-view observations of an instance into a single semantic descriptor, which is insufficient to represent a complete 3D instance. To retain rich semantic information across diverse viewpoints, we propose MSR-3D, a framework built around a Multi-Mode Semantic Representation. Specifically, MSR-3D derives multi-view observations from both 2D segmentation masks and rendered masks of each segmented 3D instance. These observations are organized into groups. Each group is summarized as a semantic mode through a representative feature and associated statistical attributes. Based on the multi-mode semantic representation, we further introduce a lightweight learnable mode-aware matcher for querying. The matcher employs linear scoring modules to project the multi-mode representation of each query-instance pair into a three-dimensional score space. Calibrated decision boundaries are then learned in the resulting score space to separate true query-instance matches from semantically related distractors. This structured and decomposable design further improves the interpretability and generalization of MSR-3D. Experiments on open-vocabulary scene-understanding benchmarks show that MSR-3D achieves state-of-the-art results, improving mIoU on LERF-OVS by 7.52% over the baseline and achieving the best overall performance on 3DOVS.