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We present Diff3F as a simple, robust, and class-agnostic feature descriptorthat can be computed for untextured input shapes (meshes or point clouds). Ourmethod distills diffusion features from image foundational models onto inputshapes. Specifically, we use the input shapes to produce depth and normal mapsas guidance for conditional image synthesis, and in the process produce(diffusion) features in 2D that we subsequently lift and aggregate on theoriginal surface. Our key observation is that even if the conditional imagegenerations obtained from multi-view rendering of the input shapes areinconsistent, the associated image features are robust and can be directlyaggregated across views. This produces semantic features on the input shapes,without requiring additional data or training. We perform extensive experimentson multiple benchmarks (SHREC'19, SHREC'20, and TOSCA) and demonstrate that ourfeatures, being semantic instead of geometric, produce reliable correspondenceacross both isometeric and non-isometrically related shape families.

Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features