论文标题
通过Riemannian度量保存可变形的表面重建
Deformable Surface Reconstruction via Riemannian Metric Preservation
论文作者
论文摘要
从单眼图像中估算物体的姿势是计算机视觉中基本的反问题。这个问题的不良性质要求将变形先验纳入解决方案。实际上，在操纵时，许多材料不会明显地收缩或扩展，构成了一个强大而众所周知的先验。从数学上讲，这转化为Riemannian指标的保存。神经网络提供了解决表面重建问题的理想操场，因为它们可以以任意精度近似表面并允许计算差异几何数量。本文提出了一种从一系列图像中推断出连续可变形表面的方法，该图像对几种技术进行了基准测试，并获得了最先进的性能而无需离线训练。
Estimating the pose of an object from a monocular image is an inverse problem fundamental in computer vision. The ill-posed nature of this problem requires incorporating deformation priors to solve it. In practice, many materials do not perceptibly shrink or extend when manipulated, constituting a powerful and well-known prior. Mathematically, this translates to the preservation of the Riemannian metric. Neural networks offer the perfect playground to solve the surface reconstruction problem as they can approximate surfaces with arbitrary precision and allow the computation of differential geometry quantities. This paper presents an approach to inferring continuous deformable surfaces from a sequence of images, which is benchmarked against several techniques and obtains state-of-the-art performance without the need for offline training.
