Robust Low-Poly Meshing for General 3D Models

Author:

Chen Zhen12ORCID,Pan Zherong3ORCID,Wu Kui4ORCID,Vouga Etienne1ORCID,Gao Xifeng3ORCID

Affiliation:

1. University of Texas at Austin, Austin, United States of America

2. LightSpeed Studios, Bellevue, WA, USA

3. LightSpeed Studios, Bellevue, WA, United States of America

4. LightSpeed Studios, Los Angeles, CA, United States of America

Abstract

We propose a robust re-meshing approach that can automatically generate visual-preserving low-poly meshes for any high-poly models found in the wild. Our method can be seamlessly integrated into current mesh-based 3D asset production pipelines. Given an input high-poly, our method proceeds in two stages: 1) Robustly extracting an offset surface mesh that is feature-preserving, and guaranteed to be watertight, manifold, and self-intersection free; 2) Progressively simplifying and flowing the offset mesh to bring it close to the input. The simplicity and the visual-preservation of the generated low-poly is controlled by a user-required target screen size of the input: decreasing the screen size reduces the element count of the low-poly but enlarges its visual difference from the input. We have evaluated our method on a subset of the Thingi10K dataset that contains models created by practitioners in different domains, with varying topological and geometric complexities. Compared to state-of-the-art approaches and widely used software, our method demonstrates its superiority in terms of the element count, visual preservation, geometry, and topology guarantees of the generated low-polys.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Reference77 articles.

1. Donya Labs AB. 2022. Simplygon 9. https://www.simplygon.com/Home/Index#section-solutions Donya Labs AB. 2022. Simplygon 9. https://www.simplygon.com/Home/Index#section-solutions

2. A lightweight approach to repairing digitized polygon meshes

3. On converting sets of tetrahedra to combinatorial and PL manifolds

4. Fast winding numbers for soups and clouds

5. Kinetic Shape Reconstruction

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3