Extracting layers and analyzing their specular properties using epipolar-plane-image analysis
Extracting layers and analyzing their specular properties using epipolar-plane-image analysis
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DOI：
10.1016/j.cviu.2004.06.001
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发表时间：
2005-01-01
影响因子：
4.5
通讯作者：
Anandan, P
中科院分区：
文献类型：
--
作者：
Criminisi, A;Kang, SB;Anandan, P
尽管在立体重建和运动结构方面取得了进展，但从多幅图像中重建3D场景仍然面临许多困难，特别是在处理遮挡、部分可见性、无纹理区域和镜面反射方面。此外，从许多视图中恢复空间密集3D表示的问题还没有得到充分的处理。本文解决了从一系列图像中实现密集重建以及分析和去除镜面高光的问题。第一部分描述了一种通过分析极平面图像（EPI）体积，将场景自动分解为一组时空层（即EPI管）的方法。我们方法的关键是直接利用EPI卷中发现的高度规律性。与以往在EPI卷上专注于稀疏特征轨迹集的工作不同，我们开发了一个完整而密集的EPI卷分割。提出了两种不同的算法来分割输入EPI体积到其组成的EPI管。第二部分描述了EPI框架内镜面反射的数学特征，并提出了一种将静态场景分解为其漫射（朗伯）和镜面组件的新技术。此外，提出了一种基于光度特性的反射物分类方法，作为设计进一步分离技术的指导。我们的方法的有效性证明了一系列复杂的场景序列与大量的遮挡和镜面。特别是，我们演示了对象的移除和插入，深度图估计，以及镜面高光的检测和移除。(C) 2004爱思唯尔公司版权所有。
Despite progress in stereo reconstruction and structure from motion, 3D scene reconstruction from multiple images still faces many difficulties, especially in dealing with occlusions, partial visibility, textureless regions, and specular reflections. Moreover, the problem of recovering a spatially dense 3D representation from many views has not been adequately treated. This document addresses the problems of achieving a dense reconstruction from a sequence of images and analyzing and removing specular highlights. The first part describes an approach for automatically decomposing the scene into a set of spatio-temporal layers (namely EPI-tubes) by analyzing the epipolar plane image (EPI) volume. The key to our approach is to directly exploit the high degree of regularity found in the EPI volume. In contrast to past work on EPI volumes that focused on a sparse set of feature tracks, we develop a complete and dense segmentation of the EPI volume. Two different algorithms are presented to segment the input EPI volume into its component EPI tubes. The second part describes a mathematical characterization of specular reflections within the EPI framework and proposes a novel technique for decomposing a static scene into its diffuse (Lambertian) and specular components. Furthermore, a taxonomy of specularities based on their photometric properties is presented as a guide for designing further separation techniques. The validity of our approach is demonstrated on a number of sequences of complex scenes with large amounts of occlusions and specularity. In particular, we demonstrate object removal and insertion, depth map estimation, and detection and removal of specular highlights. (C) 2004 Elsevier Inc. All rights reserved.