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Bibliographic Details
Main Authors: Vu, Brandon, Ganguly, Shameek, Joshi, Pushkar
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2509.22847
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author Vu, Brandon
Ganguly, Shameek
Joshi, Pushkar
author_facet Vu, Brandon
Ganguly, Shameek
Joshi, Pushkar
contents Simplifying complex 3D meshes is a crucial step in robotics applications to enable efficient motion planning and physics simulation. Common methods, such as approximate convex decomposition, represent a mesh as a collection of simple parts, which are computationally inexpensive to simulate. However, existing approaches apply a uniform error tolerance across the entire mesh, which can result in a sub-optimal trade-off between accuracy and performance. For instance, a robot grasping an object needs high-fidelity geometry in the vicinity of the contact surfaces but can tolerate a coarser simplification elsewhere. A uniform tolerance can lead to excessive detail in non-critical areas or insufficient detail where it's needed most. To address this limitation, we introduce Empart, an interactive tool that allows users to specify different simplification tolerances for selected regions of a mesh. Our method leverages existing convex decomposition algorithms as a sub-routine but uses a novel, parallelized framework to handle region-specific constraints efficiently. Empart provides a user-friendly interface with visual feedback on approximation error and simulation performance, enabling designers to iteratively refine their decomposition. We demonstrate that our approach significantly reduces the number of convex parts compared to a state-of-the-art method (V-HACD) at a fixed error threshold, leading to substantial speedups in simulation performance. For a robotic pick-and-place task, Empart-generated collision meshes reduced the overall simulation time by 69% compared to a uniform decomposition, highlighting the value of interactive, region-specific simplification for performant robotics applications.
format Preprint
id arxiv_https___arxiv_org_abs_2509_22847
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Empart: Interactive Convex Decomposition for Converting Meshes to Parts
Vu, Brandon
Ganguly, Shameek
Joshi, Pushkar
Robotics
Simplifying complex 3D meshes is a crucial step in robotics applications to enable efficient motion planning and physics simulation. Common methods, such as approximate convex decomposition, represent a mesh as a collection of simple parts, which are computationally inexpensive to simulate. However, existing approaches apply a uniform error tolerance across the entire mesh, which can result in a sub-optimal trade-off between accuracy and performance. For instance, a robot grasping an object needs high-fidelity geometry in the vicinity of the contact surfaces but can tolerate a coarser simplification elsewhere. A uniform tolerance can lead to excessive detail in non-critical areas or insufficient detail where it's needed most. To address this limitation, we introduce Empart, an interactive tool that allows users to specify different simplification tolerances for selected regions of a mesh. Our method leverages existing convex decomposition algorithms as a sub-routine but uses a novel, parallelized framework to handle region-specific constraints efficiently. Empart provides a user-friendly interface with visual feedback on approximation error and simulation performance, enabling designers to iteratively refine their decomposition. We demonstrate that our approach significantly reduces the number of convex parts compared to a state-of-the-art method (V-HACD) at a fixed error threshold, leading to substantial speedups in simulation performance. For a robotic pick-and-place task, Empart-generated collision meshes reduced the overall simulation time by 69% compared to a uniform decomposition, highlighting the value of interactive, region-specific simplification for performant robotics applications.
title Empart: Interactive Convex Decomposition for Converting Meshes to Parts
topic Robotics
url https://arxiv.org/abs/2509.22847