McGrids: Monte Carlo-Driven Adaptive Grids for Iso-Surface Extraction

TL;DR

McGrids introduces a Monte Carlo-based adaptive grid method for efficient iso-surface extraction, significantly reducing computational costs and memory usage while maintaining high-quality geometric detail.

Contribution

The paper presents a novel Monte Carlo-driven adaptive grid approach for iso-surface extraction, improving efficiency over uniform grid methods.

Findings

Reduces implicit field queries significantly

Decreases memory usage substantially

Produces high-quality, detailed meshes

Abstract

Iso-surface extraction from an implicit field is a fundamental process in various applications of computer vision and graphics. When dealing with geometric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction rather than using a simple uniform grid as prior art does. Specifically, we formulate the problem of constructing adaptive grids as a probability sampling problem, which is then solved by Monte Carlo process. We demonstrate McGrids' capability with extensive experiments from both analytical SDFs computed from surface meshes and learned implicit fields from real multiview images. The experiment results show that our McGrids can significantly reduce the number of implicit field queries, resulting in significant memory reduction, while producing high-quality meshes with rich geometric details.