A probabilistic framework for surface reconstruction from multiple images
A probabilistic framework for surface reconstruction from multiple images
复制标题
用于从多个图像进行表面重建的概率框架
DOI：
10.1109/cvpr.2001.990999
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
L. Davis
中科院分区：
文献类型：
--
作者：
M. Agrawal;L. Davis
本文提出了一种新的概率框架，从多个立体图像的三维表面重建。该方法对场景的离散体素化表示起作用。一个迭代方案被用来估计场景点位于真实3D表面上的概率。我们的方法的新奇在于能够建模和恢复表面可能被遮挡在某些视图。这是通过明确估计3D场景点在来自给定图像的集合的特定视图中可见的概率来完成的。这依赖于这样一个事实，即对于朗伯曲面上的一个点，如果其沿沿着两个视图的投影的像素强度不同，则该点必然在其中一个视图中被遮挡。我们目前的结果，表面重建从真实的和合成图像集。
The paper presents a novel probabilistic framework for 3D surface reconstruction from multiple stereo images. The method works on a discrete voxelized representation of the scene. An iterative scheme is used to estimate the probability that a scene point lies on the true 3D surface. The novelty of our approach lies in the ability to model and recover surfaces which may be occluded in some views. This is done by explicitly estimating the probabilities that a 3D scene point is visible in a particular view from the set of given images. This relies on the fact that for a point on a lambertian surface, if the pixel intensities of its projection along two views differ, then the point is necessarily occluded in one of the views. We present results of surface reconstruction from both real and synthetic image sets.