Shrinking: Reconstruction of Parameterized Surfaces from Signed Distance Fields

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

We propose a novel method for reconstructing explicit parameterized surfaces from Signed Distance Fields (SDFs), a widely used implicit neural representation (INR) for 3D surfaces. While traditional reconstruction methods like Marching Cubes extract discrete meshes that lose the continuous and differentiable properties of INRs, our approach iteratively contracts a parameterized initial sphere to conform to the target SDF shape, preserving differentiability and surface parameterization throughout. This enables downstream applications such as texture mapping, geometry processing, animation, and finite element analysis. Evaluated on the typical geometric shapes and parts of the ABC dataset, our method achieves competitive reconstruction quality, maintaining smoothness and differentiability crucial for advanced computer graphics and geometric deep learning applications.

Identifier

105001049869 (Scopus)

ISBN

[9798350374889]

Publication Title

Proceedings - 2024 International Conference on Machine Learning and Applications, ICMLA 2024

External Full Text Location

https://doi.org/10.1109/ICMLA61862.2024.00166

First Page

1095

Last Page

1100

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