Proximity-Preserving Neural Subdivision

2026-08-10

AI Summary

This study addresses the limitations of poor fitting in traditional template methods and structural instability in unconstrained neural subdivision by proposing a neighbor-preserving neural subdivision framework. Building upon Loop subdivision, the method introduces bounded curvature gating corrections alongside covariant local coordinate systems and differentiable operators to architecturally guarantee rigid-body equivariance, exact planar reproduction, and neighbor envelope constraints. Experimental results demonstrate that this approach significantly enhances ridge feature approximation accuracy while maintaining structural stability across multiple subdivision levels and eliminating high-frequency artifacts. Ultimately, this work achieves an effective unification of feature fitting capabilities with rigorous geometric structural constraints, offering a robust solution for high-fidelity surface reconstruction.

Abstract

Classical subdivision schemes are widely used because they are local, repeatable, and analytically tractable. A single stencil defines the entire refinement rule, and the behaviour of the resulting operator under iteration is well understood. This uniformity, however, means that fixed stencils tend to underfit localised geometric features, such as sharp ridges or soft edges, where curvature is concentrated. Neural mesh refinement can adapt to such features, yet unconstrained vertex prediction usually lacks the structural behaviour required of a subdivision operator once the refinement rule is applied to its own output. In this work, we introduce Proximity-Preserving Neural Subdivision, or PNS for short. PNS is a trainable refinement rule that augments Loop subdivision with a small, bounded, curvature-gated correction expressed in a covariant local frame. The construction is designed so that, for any finite network weights, the operator is exactly equivariant under rigid motion, reproduces planar input exactly, and remains inside a quadratic proximity envelope around the Loop stencil. At planar valence-k stars, the linearised operator agrees with Loop, and it therefore inherits Loop's tangent eigenspaces and Reif spectral gap at that reference configuration. All of these properties are architectural and hold before any training takes place. Empirically, PNS improves the approximation of localised ridge features while remaining inside its prescribed proximity envelope under repeated subdivision. An unconstrained neural baseline, in contrast, achieves stronger one-step fitting but develops high-frequency artefacts and leaves the subdivision regime once iterated. The overall message of this work is that learning can be introduced into subdivision without abandoning the structural constraints that make subdivision useful as a geometry-processing primitive.

Problem

• Neural Subdivision
• Mesh Refinement
• Geometric Features
• Structural Constraints
• Proximity-Preserving

Innovation

• Neural Subdivision
• Proximity-Preserving
• Equivariance
• Loop Subdivision
• Structural Constraints