Walking Through 3D Spaces: Spatial Routing for Referring 3D Gaussian Splatting Segmentation

This paper introduces Walk3D, a novel 3D Gaussian Splatting (3DGS) framework explicitly designed for 3D referring segmentation, particularly excelling at interpreting complex, long-sentence descriptions. Our primary motivation stems from observing that existing 3DGS-based open-vocabulary methods mainly focus on simple, category-level object segmentation. These methods struggle to precisely segment targets described by highly detailed, multi-sentence queries due to weak compositional language understanding and a lack of structural scene awareness. To tackle these dense and complex linguistic constraints, we propose a spatial routing network that decodes these queries into sequential chain-of-thought instructions upon a scene graph. Specifically, we construct a static supernode graph based on the geometric and semantic properties of the 3DGS space, where a spatial Graph Neural Network (GNN) dynamically pathfinds using the decomposed linguistic chain to accurately segment the referred target. Extensive experiments on complex 3D referring segmentation benchmarks demonstrate the superiority of our proposed method in interpreting lengthy, multi-conditional queries. Our code and trained models will be publicly released.