基于阴影的RGB-D图像形状细化

L. Yu, Sai-Kit Yeung, Yu-Wing Tai, Stephen Lin
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引用次数: 125

摘要

我们提出了一种基于阴影的形状优化算法,该算法使用来自Kinect的噪声,不完整的深度图来帮助解决阴影形状中的歧义。在我们的框架中,部分深度信息用于克服在法线估计中的浅地形模糊,以及协助恢复相对反照率,这需要可靠地估计照明环境并将阴影与反照率分开。使用噪声深度图对表面法线进行细化,可以获得高质量的3D表面。我们的算法的有效性通过几个具有挑战性的现实世界的例子来证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shading-Based Shape Refinement of RGB-D Images
We present a shading-based shape refinement algorithm which uses a noisy, incomplete depth map from Kinect to help resolve ambiguities in shape-from-shading. In our framework, the partial depth information is used to overcome bas-relief ambiguity in normals estimation, as well as to assist in recovering relative albedos, which are needed to reliably estimate the lighting environment and to separate shading from albedo. This refinement of surface normals using a noisy depth map leads to high-quality 3D surfaces. The effectiveness of our algorithm is demonstrated through several challenging real-world examples.
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