GARField: Group Anything with Radiance Fields
Chung Min Kim Mingxuan Wu Justin Kerr Ken Goldberg Matthew Tancik Angjoo Kanazawa
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. See the project website at https://www.garfield.studio/