Learning Neural Implicit Representations with Surface Signal Parameterizations

TL;DR

This paper introduces a neural network architecture that encodes surface parameterizations for implicit 3D surface representations, enabling effective texture mapping and appearance modeling compatible with existing mesh-based content.

Contribution

We propose a novel weight-encoded neural implicit surface model that incorporates surface parameterizations, enhancing appearance rendering and texture mapping capabilities.

Findings

Outperforms baseline methods in appearance quality.

Enables texture mapping on neural implicit surfaces.

Compatible with existing mesh-based appearance data.

Abstract

Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations commonly couple the 3D shape data with auxiliary surface-mapped image data, such as diffuse color textures and fine-scale geometric details in normal maps that typically require a mapping of the 3D surface onto a plane, i.e., a surface parameterization; implicit representations, on the other hand, cannot be easily textured due to lack of configurable surface parameterization. Inspired by this digital content authoring methodology, we design a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data. As such, our model remains compatible with existing mesh-based digital content with appearance data. Motivated by recent work that overfits compact networks to individual 3D objects, we present a new weight-encoded neural implicit representation that extends the capability of neural implicit surfaces to enable various common and important applications of texture mapping. Our method outperforms reasonable baselines and state-of-the-art alternatives.