Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
<|reference_start|>Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction: 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<|reference_end|>
@article{attaiki2024shape,
    title={Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape
  Matching via Unsupervised Functional Map Regularized Reconstruction},
    author={Souhaib Attaiki, Maks Ovsjanikov},
    journal={2023 Advances in Neural Information Processing Systems (NeurIPS)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06804},
    primaryClass={cs.CV}
}