  We explore the task of zero-shot semantic segmentation of 3D shapes by using
large-scale off-the-shelf 2D image recognition models. Surprisingly, we find
that modern zero-shot 2D object detectors are better suited for this task than
contemporary text/image similarity predictors or even zero-shot 2D segmentation
networks. Our key finding is that it is possible to extract accurate 3D
segmentation maps from multi-view bounding box predictions by using the
topological properties of the underlying surface. For this, we develop the
Segmentation Assignment with Topological Reweighting (SATR) algorithm and
evaluate it on ShapeNetPart and our proposed FAUST benchmarks. SATR achieves
state-of-the-art performance and outperforms a baseline algorithm by 1.3% and
4% average mIoU on the FAUST coarse and fine-grained benchmarks, respectively,
and by 5.2% average mIoU on the ShapeNetPart benchmark. Our source code and
data will be publicly released. Project webpage:
https://samir55.github.io/SATR/.
