TOFUGS: TOPOLOGY BEFORE FUSION FOR OPEN-VOCABULARY GAUSSIAN SPLATTING

Compact open-vocabulary 3D representations must consolidate multiview, multiscale semantics into one deployable descriptor per primitive. A common Gaussian splatting pipeline fuses these observations before semantic refinement, implicitly treating fusion as an independent step of refinement. However, this ordering matters because mask granularity can change feature directions as well as the induced refinement topology: fusion before topology constructs a single topology from mixed features and removes branch-specific information, whereas topology before fusion preserves the complementary partitions and mask registrations induced by each branch. To address this problem, we introduce ToFuGS (Topology-before-Fusion Gaussian Splatting), which independently refines branch-specific mask-derived fields first and uses training-view evidence to fuse them into a single deployable descriptor per Gaussian. On LERF, ToFuGS improves its underlying refinement pipeline by 7.34 mIoU points and establishes a new state of the art. Without retuning, it improves results on 3D-OVS, and an order control with a second refinement backend likewise favors refine-then-fuse.