Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion. (arXiv:2207.14782v1 [cs.CV])

Explicit neural surface representations allow for exact and efficient
extraction of the encoded surface at arbitrary precision, as well as analytic
derivation of differential geometric properties such as surface normal and
curvature. Such desirable properties, which are absent in its implicit
counterpart, makes it ideal for various applications in computer vision,
graphics and robotics. However, SOTA works are limited in terms of the topology
it can effectively describe, distortion it introduces to reconstruct complex
surfaces and model efficiency. In this …

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