S4R: Scaling for Rigid-Body Interpenetration Resolution

Rigid-body interpenetration frequently occurs in procedurally assembled and generated scenes and must be removed before downstream applications such as physical simulation. We present S4R ( S caling for Rigid-Body Interpenetration R esolution), a scale-continuation method for static interpenetration repair. S4R first uniformly shrinks each body about a fixed reference center to a small initial scale, at which the layout is penetration-free, and then restores full scale through a sequence of minimum-norm convex contact quadratic programs (QPs) that target the linearized separation margin during continuation. Resolution thereby replaces one deep correction with a sequence of shallow-contact subproblems. A conservative scale-event bound and frozen-witness gap predictions cut the number of exact mesh queries; the continuation then ends with a full-scale evaluator check and bounded tail refinement. We evaluate S4R on Kubric [Greff et al. 2022], HY3D-Bench [Team Hunyuan3D et al. 2026], and Thingi10K [Zhou and Jacobson 2016] using a shared mesh-level evaluator and a unified per-scene timing protocol. In the main comparisons on all three benchmarks, up to N =5000 bodies, S4R reaches zero reported penetration with displacement that stays small and nearly independent of scene size, and at the lowest wall time within each hardware tier among the compared methods. A GPU implementation extends these results to large-scale scenes. Our code and data can be found on our project page: https://frank-zy-dou.github.io/projects/S4R/index.html.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Graphics
Published
2026-11-09
DOI
https://doi.org/10.1145/3842510
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

S4R: Scaling for Rigid-Body Interpenetration Resolution

Haixu Wu, Wojciech Matusik, Cheng Lin, Wenping Wang et al.
ACM Transactions on Graphics
Computer Graphics and Visualization Techniques
article

S4R: Scaling for Rigid-Body Interpenetration Resolution

Haixu Wu, Wojciech Matusik, Cheng Lin, Wenping Wang, Zhiyang Dou, Minghao Guo, Xiaohu Guo, Junfeng Yao, Chen Peng, Yuan Liu, Ang Zhao
article en

Abstract

Rigid-body interpenetration frequently occurs in procedurally assembled and generated scenes and must be removed before downstream applications such as physical simulation. We present S4R ( S caling for Rigid-Body Interpenetration R esolution), a scale-continuation method for static interpenetration repair. S4R first uniformly shrinks each body about a fixed reference center to a small initial scale, at which the layout is penetration-free, and then restores full scale through a sequence of minimum-norm convex contact quadratic programs (QPs) that target the linearized separation margin during continuation. Resolution thereby replaces one deep correction with a sequence of shallow-contact subproblems. A conservative scale-event bound and frozen-witness gap predictions cut the number of exact mesh queries; the continuation then ends with a full-scale evaluator check and bounded tail refinement. We evaluate S4R on Kubric [Greff et al. 2022], HY3D-Bench [Team Hunyuan3D et al. 2026], and Thingi10K [Zhou and Jacobson 2016] using a shared mesh-level evaluator and a unified per-scene timing protocol. In the main comparisons on all three benchmarks, up to N =5000 bodies, S4R reaches zero reported penetration with displacement that stays small and nearly independent of scene size, and at the lowest wall time within each hardware tier among the compared methods. A GPU implementation extends these results to large-scale scenes. Our code and data can be found on our project page: https://frank-zy-dou.github.io/projects/S4R/index.html.

ACM Transactions on GraphicsVol. 45(6)
Macau University of Science and Technology (MO), The University of Texas at Dallas (US), Xiamen University (CN), Hong Kong University of Science and Technology (HK), MIT Computer Science and Artificial Intelligence Laboratory (US), Massachusetts Institute of Technology (US), University of Hong Kong (HK), Texas A&M University (US)
Fundo para o Desenvolvimento das Ciências e da Tecnologia
Computer Graphics and Visualization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon