Shape Non-Rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional mAP Regularized Reconstruction

Abstract

We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data.SNK operates on a single pair of shapes, and employs a reconstruction-based strategy using an encoder-decoder architecture, which deforms the source shape to closely match the target shape. During the process, an unsupervised functional map is predicted and converted into a point-to-point map, serving as a supervisory mechanism for the reconstruction. To aid in training, we have designed a new decoder architecture that generates smooth, realistic deformations. SNK demonstrates competitive results on traditional benchmarks, simplifying the shape-matching process without compromising accuracy. Our code can be found online: https://github.com/pvnieo/SNK

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Cite

Text

Attaiki and Ovsjanikov. "Shape Non-Rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional mAP Regularized Reconstruction." Neural Information Processing Systems, 2023.

Markdown

[Attaiki and Ovsjanikov. "Shape Non-Rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional mAP Regularized Reconstruction." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/attaiki2023neurips-shape/)

BibTeX

@inproceedings{attaiki2023neurips-shape,
  title     = {{Shape Non-Rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional mAP Regularized Reconstruction}},
  author    = {Attaiki, Souhaib and Ovsjanikov, Maks},
  booktitle = {Neural Information Processing Systems},
  year      = {2023},
  url       = {https://mlanthology.org/neurips/2023/attaiki2023neurips-shape/}
}