论文标题
从单眼视频中重建手持物体
Reconstructing Hand-Held Objects from Monocular Video
论文作者
论文摘要
本文提出了一种方法，该方法可以从单眼视频中重建手持物体。与许多最近通过训练有素的网络预测对象几何形状的最新方法相反，所提出的方法不需要对对象的任何知识，并且能够恢复更准确，更详细的对象几何形状。关键的想法是，手运动自然提供对象的多种视图，并且可以通过手动姿势跟踪器可靠地估计运动。然后，可以通过解决多视图重建问题来恢复对象几何形状。我们设计了一种基于隐式神经表示的方法，以解决重建问题并解决不精确的手姿势估计，相对手动运动以及对小物体的几何形状优化不足的问题。我们还提供了一个新收集的数据集，具有3D地面真相来验证所提出的方法。
This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach.
