单图重建可动3D物体，通过逐步推理结构提升精度与效率
MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction
- 分步推理：从图像逐步构建标准几何、部件结构和运动感知表征
- 在PartNet-Mobility上达到最优重建精度与最快推理速度
- 无需外部运动模板，适用于机器人操作与复杂场景重建
从单张图像重建可动3D物体需同时推断几何形状、部件结构与运动参数，核心难点在于运动线索与结构之间的纠缠，导致直接回归关节参数不稳定。现有方法依赖多视角监督、基于检索的组装或辅助视频生成，常牺牲可扩展性或效率。本文提出MonoArt，一个基于渐进式结构推理的统一框架。不直接从图像特征预测关节，而是通过单一架构逐步将视觉观察转化为标准几何、结构化部件表示和运动感知嵌入。该结构化推理过程实现稳定且可解释的关节推断，无需外部运动模板或多阶段流程。在PartNet-Mobility上的大量实验表明，MonoArt在重建精度与推理速度上均达当前最优。框架还成功推广至机器人操作与可动场景重建任务。
原文摘要 · Abstract (English)
Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi-view supervision, retrieval-based assembly, or auxiliary video generation, often sacrificing scalability or efficiency. We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. This structured reasoning process enables stable and interpretable articulation inference without external motion templates or multi-stage pipelines. Extensive experiments on PartNet-Mobility demonstrate that OM achieves state-of-the-art performance in both reconstruction accuracy and inference speed. The framework further generalizes to robotic manipulation and articulated scene reconstruction.
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