Newest papers

SceneRig: An Agentic System for Simulation-Ready 3D Scene Reconstruction from Single Images

Rundong Luo, Yunong Liu, Di Cao, Matthew Tancik, Shyamal Buch, Colton Stearns, Wenqi Xian · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28585 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

模拟是机器人技术的强大工具,但在规模上构建多样化的模拟准备的场景仍然是困难的。从单个图像中重建场景是一个有前途的替代方案,但仅视觉相似性是不够的:对象也必须得到恢复,准确放置,并物理地理化。依靠后期物理学的现有方法往往会错误地定位对象,产生看起来正确但在操纵下表现不佳的场景。我们介绍SceneRig,一个代理系统,将单个图像变成可编辑的,模拟准备的场景。SceneRig结合地理,视觉和物理工具来重建一个忠实的3D场景。一个代理然后在模拟器内部执行编辑和验证循环,以实现物理稳定性

Machine translation (Meta M2M100); the English original follows.

Abstract

Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from single images is a promising alternative, but visual similarity alone is insufficient: objects must also be recovered, accurately placed, and physically grounded. Existing methods that rely on post-hoc physics settling often misplace objects, producing scenes that look correct but behave poorly under manipulation. We present SceneRig, an agentic system that turns a single image into an editable, simulation-ready scene. SceneRig combines geometric, visual, and physical tools to reconstruct a faithful 3D scene. An agent then performs edit-and-validate loops inside a physics simulator to achieve physical stability. Across diverse inputs, SceneRig improves instance segmentation, object-centric appearance, and 3D placement while keeping the reconstructed scenes physically stable. Furthermore, SceneRig is effective for downstream robotics: its scenes predict real-robot policy outcomes more accurately than the baseline method and more reliably support replay of recorded manipulations without trajectory adaptation.

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Topics: Computer vision, Artificial intelligence, Computer science, Trajectory, Scale (ratio), Similarity (geometry)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange