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GARField: Group Anything with Radiance Fields

Chung Min Kim · Mingxuan Wu · Justin Kerr · Ken Goldberg · Matthew Tancik · Angjoo Kanazawa

Arch 4A-E Poster #191
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Fri 21 Jun 10:30 a.m. PDT — noon PDT


Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene --- should the wheels of an excavator be considered separate or part of the whole? We present Group Anything with Radiance Fields (GARField), an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field, a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects, objects, and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding.

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