在遮挡下利用生成先验和接触诱导约束进行物体重建
Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints
AI总结：
本文提出了一种结合生成先验和接触诱导约束的方法，用于在遮挡下提高物体重建的准确性。
摘要：
物体几何是机器人操作的关键信息。然而，物体重建是一个具有挑战性的任务，因为摄像机只能捕捉到物体的部分观察结果，尤其是在发生遮挡时。在本文中，我们利用两种额外的信息来源来减少视觉信号的歧义。首先，生成模型学习了常见物体形状的先验知识，使我们能够对未见部分的几何结构做出合理的猜测。其次，接触信息可以从视频和物理交互中获得，为几何边界提供了稀疏约束。我们通过接触引导的3D生成将这两种信息结合起来。指导公式受到生成模型中基于拖动的编辑的启发。在合成和真实世界数据上的实验表明，我们的方法相比纯3D生成和基于接触的优化在重建方面有所改进。
英文摘要：
Object geometry is key information for robot manipulation. Yet, object reconstruction is a challenging task because camera observations are partial due to occlusions. The scene may not offer the flexibility for a robot to alter its viewpoint to obtain a full observation of the object of interest. In this paper, we leverage two extra sources of information to reduce the ambiguity of vision signals under occlusion. First, generative models learn priors of the shapes of commonly seen objects, allowing us to make reasonable guesses of the unseen part of geometry. Second, contact information, which can be obtained from videos and physical interactions, provides sparse constraints on the boundary of the geometry. We combine the two sources of information through contact-guided 3D generation. The guidance formulation is inspired by drag-based generative image editing. We explore different guidance strategies and highlight the importance of short gradient paths for guided generation. Experiments on synthetic and real-world data show that our approach improves the object reconstruction compared to pure 3D generation and contact-based optimization methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
