  We propose a novel zero-shot approach to computing correspondences between 3D
shapes. Existing approaches mainly focus on isometric and near-isometric shape
pairs (e.g., human vs. human), but less attention has been given to strongly
non-isometric and inter-class shape matching (e.g., human vs. cow). To this
end, we introduce a fully automatic method that exploits the exceptional
reasoning capabilities of recent foundation models in language and vision to
tackle difficult shape correspondence problems. Our approach comprises multiple
stages. First, we classify the 3D shapes in a zero-shot manner by feeding
rendered shape views to a language-vision model (e.g., BLIP2) to generate a
list of class proposals per shape. These proposals are unified into a single
class per shape by employing the reasoning capabilities of ChatGPT. Second, we
attempt to segment the two shapes in a zero-shot manner, but in contrast to the
co-segmentation problem, we do not require a mutual set of semantic regions.
Instead, we propose to exploit the in-context learning capabilities of ChatGPT
to generate two different sets of semantic regions for each shape and a
semantic mapping between them. This enables our approach to match strongly
non-isometric shapes with significant differences in geometric structure.
Finally, we employ the generated semantic mapping to produce coarse
correspondences that can further be refined by the functional maps framework to
produce dense point-to-point maps. Our approach, despite its simplicity,
produces highly plausible results in a zero-shot manner, especially between
strongly non-isometric shapes. Project webpage:
https://samir55.github.io/3dshapematch/.
