Hierarchical Collaborative Fusion for 3D Instance-aware Referring Expression Segmentation
This work addresses a domain-specific challenge in 3D scene understanding for applications like robotics or augmented reality, representing an incremental improvement over existing methods.
The paper tackles the problem of localizing objects in 3D scenes using natural language descriptions, even when descriptions are ambiguous, by proposing HCF-RES, a multi-modal framework that integrates 2D visual semantics with 3D geometry, achieving state-of-the-art results on ScanRefer and Multi3DRefer benchmarks.
Generalised 3D Referring Expression Segmentation (3D-GRES) localizes objects in 3D scenes based on natural language, even when descriptions match multiple or zero targets. Existing methods rely solely on sparse point clouds, lacking rich visual semantics for fine-grained descriptions. We propose HCF-RES, a multi-modal framework with two key innovations. First, Hierarchical Visual Semantic Decomposition leverages SAM instance masks to guide CLIP encoding at dual granularities -- pixel-level and instance-level features -- preserving object boundaries during 2D-to-3D projection. Second, Progressive Multi-level Fusion integrates representations through intra-modal collaboration, cross-modal adaptive weighting between 2D semantic and 3D geometric features, and language-guided refinement. HCF-RES achieves state-of-the-art results on both ScanRefer and Multi3DRefer.