Feedforward generalizable models for implicit shape reconstruction from
unoriented point cloud present multiple advantages, including high performance
and inference speed. However, they still suffer from generalization issues,
ranging from underfitting the input point cloud, to misrepresenting samples
outside of the training data distribution, or with toplogies unseen at
training. We propose here an efficient mechanism to remedy some of these
limitations at test time. We combine the inter-shape data prior of the network
with an intra-shape regularization prior of a Nystr\"om Kernel Ridge
Regression, that we further adapt by fitting its hyperprameters to the current
shape. The resulting shape function defined in a shape specific Reproducing
Kernel Hilbert Space benefits from desirable stability and efficiency
properties and grants a shape adaptive expressiveness-robustness trade-off. We
demonstrate the improvement obtained through our method with respect to
baselines and the state-of-the-art using synthetic and real data.

通过将网络的形状间数据先验与 Nyström 内部形状正则化先验相结合，在测试时提出了一种有效的机制来解决一些限制。通过使用综合内插核来适应当前形状的超参数，得到的形状函数在形状特定的再生核希尔伯特空间中具有稳定性和高效性，并提供形状自适应的表达力和鲁棒性的权衡。通过使用合成和真实数据，我们证明了我们的方法相对于基线和现有技术的改进。