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NESI: Shape Representation via Neural Explicit Surface Intersection by Zhang, Congyi; Yang, Jinfan; Hedlin, Eric; Takikawa, Suzuran; Vining, Nicholas; Yi, Kwang Moo; Wang, Wenping; Sheffer, Alla is a scholarly article available to read on EtoBox.

What is NESI: Shape Representation via Neural Explicit Surface Intersection about?

Compressed representations of 3D shapes that are compact, accurate, and can be processed efficiently directly in compressed form, are extremely useful for digital media applications. Recent approaches in this space focus on learned implicit or parametric representations. While implicits are well suited for tasks such as in-out queries, they lack natural 2D parameterization, complicating tasks such as texture or normal mapping. Conversely, parametric representations support the latter tasks but are ill-suited for occupancy queries. We propose a novel learned alternative to these approaches, based on intersections of localized explicit, or height-field, surfaces. Since explicits can be trivially expressed both implicitly and parametrically, NESI directly supports a wider range of processing operations than implicit alternatives, including occupancy queries and parametric access. We represent input shapes using a collection of differently oriented height-field bounded half-spaces combined using volumetric Boolean intersections. We first tightly bound each input using a pair of oppositely oriented height-fields, forming a Double Height-Field (DHF) Hull. We refine this hull by intersect

Author

Zhang, Congyi; Yang, Jinfan; Hedlin, Eric; Takikawa, Suzuran; Vining, Nicholas; Yi, Kwang Moo; Wang, Wenping; Sheffer, Alla

Published

2024

Language

EN