  Shape modeling and reconstruction from raw point clouds of objects stand as a
fundamental challenge in vision and graphics research. Classical methods
consider analytic shape priors; however, their performance degraded when the
scanned points deviate from the ideal conditions of cleanness and completeness.
Important progress has been recently made by data-driven approaches, which
learn global and/or local models of implicit surface representations from
auxiliary sets of training shapes. Motivated from a universal phenomenon that
self-similar shape patterns of local surface patches repeat across the entire
surface of an object, we aim to push forward the data-driven strategies and
propose to learn a local implicit surface network for a shared, adaptive
modeling of the entire surface for a direct surface reconstruction from raw
point cloud; we also enhance the leveraging of surface self-similarities by
improving correlations among the optimized latent codes of individual surface
patches. Given that orientations of raw points could be unavailable or noisy,
we extend sign agnostic learning into our local implicit model, which enables
our recovery of signed implicit fields of local surfaces from the unsigned
inputs. We term our framework as Sign-Agnostic Implicit Learning of Surface
Self-Similarities (SAIL-S3). With a global post-optimization of local sign
flipping, SAIL-S3 is able to directly model raw, un-oriented point clouds and
reconstruct high-quality object surfaces. Experiments show its superiority over
existing methods.
