MonoArt：基于渐进结构推理的单目关节3D重建
MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction
AI总结：
MonoArt通过渐进结构推理实现单目关节3D重建，无需外部运动模板或多阶段流程，提升重建精度和推理速度，并扩展至机器人操作和关节场景重建。
摘要：
从单张图像重建关节3D物体需要联合推断物体几何、部分结构和运动参数，但运动线索与物体结构的纠缠使直接关节回归不稳定。现有方法通过多视图监督、检索式组装或辅助视频生成解决此问题，但牺牲了可扩展性或效率。我们提出MonoArt，一个基于渐进结构推理的统一框架。而非直接从图像特征预测关节，MonoArt在单一架构中逐步将视觉观测转换为标准几何、结构化部分表示和运动感知嵌入。此结构推理过程使关节推断稳定且可解释。在PartNet-Mobility上的大量实验表明，OM在重建精度和推理速度上均达到最先进水平。该框架进一步扩展至机器人操作和关节场景重建。
英文摘要：
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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