Basis Networks: Learning basis functions for free-form triangulations
Loading...
Date
2026
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We present a framework for learning compactly supported basis functions that define tangent continuous surfaces based on coarse irregular triangle meshes. The basis functions are represented as MLPs. Smoothness of the basis functions is achieved by using the values of Loop basis functions as the parameterization of the surface. Post-multiplying the value of the MLP with the Loop basis yields smooth compact support. We show that this approach works similar or better than Neural Subdivision in terms of recreating given geometry, while the runtime scales better with surface resolution and can be evaluated at arbitrary resolution.
Description
@article{10.1111:cgf.70326,
journal = {Computer Graphics Forum},
title = {{Basis Networks: Learning basis functions for free-form triangulations}},
author = {Djuren, Tobias and Alexa, Marc},
year = {2026},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70326}
}