Depth map up-sampling using cost-volume filtering
Depth map up-sampling using cost-volume filtering
复制标题
使用成本体积过滤进行深度图上采样
DOI：
10.1109/ivmspw.2013.6611912
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
K. Aizawa
中科院分区：
文献类型：
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作者：
Ji;Satoshi Ikehata;H. Yoo;M. Gelautz;K. Aizawa
由有源传感器捕获的深度图（例如，ToF相机和Kinect）通常遭受空间分辨率差、大量噪声和丢失数据的困扰。为了克服这些问题，我们提出了一种新的深度图上采样方法，它增加了原始深度图的分辨率，同时有效地抑制混叠伪影。假设注册的高分辨率纹理图像是可用的，成本-体积过滤框架被应用到这个问题。我们的实验表明，成本-体积滤波可以准确有效地生成高分辨率的深度图，同时保留不连续的对象边界，这通常是一个挑战，当应用各种最先进的算法。
Depth maps captured by active sensors (e.g., ToF cameras and Kinect) typically suffer from poor spatial resolution, considerable amount of noise, and missing data. To overcome these problems, we propose a novel depth map up-sampling method which increases the resolution of the original depth map while effectively suppressing aliasing artifacts. Assuming that a registered high-resolution texture image is available, the cost-volume filtering framework is applied to this problem. Our experiments show that cost-volume filtering can generate the high-resolution depth map accurately and efficiently while preserving discontinuous object boundaries, which is often a challenge when various state-of-the-art algorithms are applied.