@article{discovery10191492, number = {2}, journal = {Computer Graphics Forum}, year = {2024}, title = {Neural Semantic Surface Maps}, note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.}, publisher = {Wiley}, month = {May}, volume = {43}, url = {http://dx.doi.org/10.1111/cgf.15005}, abstract = {We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Lack of annotated data prohibits direct inference of 3D semantic priors; instead, current state-of-the-art methods predominantly optimize geometric properties or require varying amounts of manual annotation. To overcome the lack of annotated training data, we distill semantic matches from pre-trained vision models: our method renders the pair of untextured 3D shapes from multiple viewpoints; the resulting renders are then fed into an off-the-shelf image-matching strategy that leverages a pre-trained visual model to produce feature points. This yields semantic correspondences, which are projected back to the 3D shapes, producing a raw matching that is inaccurate and inconsistent across different viewpoints. These correspondences are refined and distilled into an inter-surface map by a dedicated optimization scheme, which promotes bijectivity and continuity of the output map. We illustrate that our approach can generate semantic surface-to-surface maps, eliminating manual annotations or any 3D training data requirement. Furthermore, it proves effective in scenarios with high semantic complexity, where objects are non-isometrically related, as well as in situations where they are nearly isometric.}, author = {Morreale, Luca and Aigerman, Noam and Kim, Vladimir G and Mitra, Niloy J}, keywords = {CCS Concepts, Computing methodologies, Shape analysis, Mesh geometry models, Feature selection} }