  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.
