USDCraft: 関節付き3Dアセットの幾何学的根拠に基づくプログラム的モデリングによるシミュレーション
USDCraft: Geometrically Grounded Programmatic Modeling of Articulated 3D Assets for Simulation
LLMが実行可能なプログラムを書いて関節付き3Dアセットを生成し、実世界の物体をシミュレーション可能なUSDアセットとして再構築するフレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Chuanrui Zhang, Zaijia Yang, Duomin Wang, Lu Shi, Daquan Zhou, Ruihua Zhang, Ziwei Wang
分類: cs.RO
原文アブストラクト
Geometrically faithful and functional articulated 3D assets are essential for real-to-sim robot manipulation, where policies trained in simulation must transfer to physical objects. Recent mesh-based methods learn to infer articulation from annotated 3D assets, but deployment remains challenging when real-world objects fall outside the training distribution or their meshes are incomplete or corrupted. To address these limitations, we formulate articulated asset reconstruction as programmatic modeling grounded in partial geometric evidence and introduce USDCraft, a framework in which a pretrained LLM writes and revises executable programs for simulation-ready articulated assets without task-specific training. We propose source geometry analysis, which converts the source mesh into a metric textual description that distinguishes observed surface from unknown space, and iterative geometric rechecking, which re-encodes each candidate in the same representation so that discrepancies point to program edits while unobserved regions remain open to completion. Visual feedback and physical authoring guidance complete the modeling process, which produces articulated USD assets with explicit physical properties that load into Isaac Sim without manual adjustment. Experiments demonstrate leading articulation recovery on two benchmarks and validate USDCraft's effectiveness for real-to-sim-to-real robot manipulation.