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Research PaperResearchia:202610.21057

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

Rundong Luo

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 ...

Submitted: October 21, 2026Subjects: AI; Artificial Intelligence

Description / Details

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.


Source: arXiv:2610.28585v1 - http://arxiv.org/abs/2610.28585v1 PDF: https://arxiv.org/pdf/2610.28585v1 Original Link: http://arxiv.org/abs/2610.28585v1

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Submission Info
Date:
Oct 21, 2026
Topic:
Artificial Intelligence
Area:
AI
Comments:
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