Object-level mapping and topological navigation reason about whole objects, yet the correspondences they rely on are computed over keypoints or pixels and only aggregated into objects afterwards. A recent line of work removes this detour by matching directly at the level of instance segments: a class-agnostic segmenter partitions each image, and per-segment descriptors are pooled from large 3D foundation models over the masks. This shifts the open question to how frozen foundation features should be processed once the unit of matching is a segment. We introduce MuViSeg, three learned matching heads: a LightGlue-style attention head on frozen MASt3R descriptors; a DPT-style multi-scale fusion module that exposes layered spatial detail from VGGT before pooling; and, as our main contribution, a joint multi-view head that attends over segments from several views at once, recovering transitive correspondences that pairwise matchers cannot express. In zero-shot evaluation on Replica and Virtual KITTI 2 across 0--180 degree viewpoint changes, our heads consistently improve over a parameter-free Sinkhorn matcher on the same backbone. Across 102 HM3D navigation episodes, direct segment matchers and sparse keypoint aggregation are statistically indistinguishable, while dense matches voted into masks lose 13.7--18.0 SPL.

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