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Neural implicit surface representations have recently emerged as popularalternative to explicit 3D object encodings, such as polygonal meshes,tabulated points, or voxels. While significant work has improved the geometricfidelity of these representations, much less attention is given to their finalappearance. Traditional explicit object representations commonly couple the 3Dshape data with auxiliary surface-mapped image data, such as diffuse colortextures and fine-scale geometric details in normal maps that typically requirea mapping of the 3D surface onto a plane, i.e., a surface parameterization;implicit representations, on the other hand, cannot be easily textured due tolack of configurable surface parameterization. Inspired by this digital contentauthoring methodology, we design a neural network architecture that implicitlyencodes the underlying surface parameterization suitable for appearance data.As such, our model remains compatible with existing mesh-based digital contentwith appearance data. Motivated by recent work that overfits compact networksto individual 3D objects, we present a new weight-encoded neural implicitrepresentation that extends the capability of neural implicit surfaces toenable various common and important applications of texture mapping. Our methodoutperforms reasonable baselines and state-of-the-art alternatives.

Learning Neural Implicit Representations with Surface Signal Parameterizations