Shape Non-rigid Kinematics (SNK)：一种通过无监督函数映射正则化重建实现非刚性形状匹配的零样本方法
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction
AI总结：
本文提出零样本非刚性形状匹配方法 SNK，利用 encoder-decoder 重建、无监督函数映射到点对点映射监督及新型 decoder，在传统基准上取得有竞争力的精度。
摘要：
我们提出了 Shape Non-rigid Kinematics (SNK)，这是一种用于非刚性形状匹配的新型零样本方法，无需大量训练或真值数据。SNK 作用于单对形状，并采用基于重建的策略，使用 encoder-decoder 架构将源形状变形以紧密匹配目标形状。在此过程中，模型预测一个无监督函数映射，并将其转换为点对点映射，作为重建的监督机制。为辅助训练，我们设计了一种新的 decoder 架构，可生成平滑、逼真的变形。SNK 在传统基准测试上展现出具有竞争力的结果，在不损害精度的情况下简化了形状匹配流程。我们的代码可在线获取：https://github.com/pvnieo/SNK
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
