Subsecond 3D Mesh Generation for Robot Manipulation

TL;DR

This paper presents a fast, end-to-end system that generates high-quality, contextually grounded 3D meshes from a single RGB-D image in under one second, advancing real-time robotic perception and manipulation.

Contribution

It introduces a novel pipeline combining open-vocabulary segmentation, diffusion-based mesh generation, and point cloud registration for rapid, accurate 3D mesh creation in robotics.

Findings

Meshes generated in under one second.

Effective in real-world manipulation tasks.

Enables practical on-demand 3D perception for robots.

Abstract

3D meshes are a fundamental representation widely used in computer science and engineering. In robotics, they are particularly valuable because they capture objects in a form that aligns directly with how robots interact with the physical world, enabling core capabilities such as predicting stable grasps, detecting collisions, and simulating dynamics. Although automatic 3D mesh generation methods have shown promising progress in recent years, potentially offering a path toward real-time robot perception, two critical challenges remain. First, generating high-fidelity meshes is prohibitively slow for real-time use, often requiring tens of seconds per object. Second, mesh generation by itself is insufficient. In robotics, a mesh must be contextually grounded, i.e., correctly segmented from the scene and registered with the proper scale and pose. Additionally, unless these contextual grounding steps remain efficient, they simply introduce new bottlenecks. In this work, we introduce an end-to-end system that addresses these challenges, producing a high-quality, contextually grounded 3D mesh from a single RGB-D image in under one second. Our pipeline integrates open-vocabulary object segmentation, accelerated diffusion-based mesh generation, and robust point cloud registration, each optimized for both speed and accuracy. We demonstrate its effectiveness in a real-world manipulation task, showing that it enables meshes to be used as a practical, on-demand representation for robotics perception and planning.