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Relaxation method for segmenting quadratic surfaces from depth and intensity images

机译:从深度和强度图像分割二次表面的放松方法

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Recovery of shape and structure of objects present in a scene from its image is a significant problem in vision. The purpose of this paper is to develop and demonstrate an iterative relaxation algorithm that combines (by a fusion process) surface interpolation using a nonuniformly sampled range image and the recovery of shape from shading of a uniformly sampled intensity image. The recovery of shape from shading requires uniform lighting, as well as uniform viewing directions across the scene which is difficult to achieve. The objective is to extract several piecewise planar surfaces whose orientation parameters are extracted iteratively. With a few exceptions, most range sensors provide nonuniformly sampled depth images. It is desirable to extract the intrinsic surfaces and resample the image over a uniformly spaced grid. Then, it is expected that the structure of each image is isomorphic to that of the other image. Once the structure based region/volume correspondence is established, it becomes possible to adapt the consistency constraint for each surface and the smoothness criterion at the boundaries between two surfaces, and to activate resegmentation (incremental) if necessary.
机译:从其图像中恢复存在于场景中存在的物体的形状和结构是视觉中的重大问题。本文的目的是开发和展示一种迭代放松算法,其使用非均匀的采样范围图像结合(通过融合过程)表面内插,以及从均匀采样的强度图像的阴影中恢复形状。从阴影中恢复形状需要均匀的照明,以及难以实现的场景中的均匀观察方向。目的是提取若干分段平面表面,其取向参数迭代地提取。通过一些例外,大多数范围传感器提供不均匀的采样深度图像。期望提取内在表面并在均匀间隔的网格上重新采样图像。然后,预期每个图像的结构与另一个图像的结构是同性的。一旦建立了基于结构的区域/卷对应关系,就可以根据需要调整每个表面的一致性约束以及在两个表面之间的边界处的平滑度标准,并在必要时激活遣散(增量)。

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