视觉振动层析成像：从单目视频估计内部材料属性
Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video
AI总结：
本文提出视觉振动层析成像方法，利用单目视频中的表面亚像素振动模态，推断已知几何形状三维物体内部空间变化的杨氏模量与密度。
摘要：
物体的内部材料属性虽然肉眼不可见，却决定了在其表面观察到的运动。我们提出一种方法，可通过拍摄物体表面振动的单目视频来估计其非均匀材料属性。具体而言，我们展示了如何在几何形状已知的三维物体内部估计杨氏模量和密度。了解这些数值在物体内部如何变化，有助于模拟其运动并表征任何缺陷。传统的无损检测方法通常需要昂贵的仪器，且一般只能估计均质化的材料属性，或仅仅识别缺陷是否存在。相比之下，我们的方法利用单目视频来：（1）从物体的亚像素运动中识别图像空间模态；（2）根据观察到的模态直接推断空间变化的杨氏模量和密度值。我们在模拟视频和真实视频上均验证了该方法。
英文摘要：
An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations. Specifically, we show how to estimate Young's modulus and density throughout a 3D object with known geometry. Knowledge of how these values change across the object is useful for simulating its motion and characterizing any defects. Traditional non-destructive testing approaches, which often require expensive instruments, generally estimate only homogenized material properties or simply identify the presence of defects. In contrast, our approach leverages monocular video to (1) identify image-space modes from an object's sub-pixel motion, and (2) directly infer spatially-varying Young's modulus and density values from the observed modes. We demonstrate our approach on both simulated and real videos.
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。
