Trilateral filter construction for depth map upsampling

Jaekwang Kim, Jaeho Lee, Seung-Ryong Han, Dowan Kim, Jongsul Min, Changick Kim
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引用次数: 6

Abstract

In recent years, fusion camera systems that consist of color cameras and Time-of-Flight (TOF) depth sensors have been popularly used due to its depth sensing capability at real-time frame rates. However, captured depth maps are limited in low resolution compared to the corresponding color images due to physical limitation of the TOF depth sensor. Although many algorithms have been proposed, they still yield erroneous results, especially when boundaries of the depth map and the color image are not aligned. We therefore propose a novel kernel regression framework to generate the high quality depth map. Our proposed filter is based on the vector pointing homogeneous pixels that represents the unit vector toward similar neighbors in the local region. The vectors are used to detect misaligned regions between color edges and depth edges. Experimental comparisons with other data fusion techniques prove the superiority of the proposed algorithm.
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用于深度图上采样的三边滤波器构建
近年来,由彩色相机和TOF (Time-of-Flight)深度传感器组成的融合相机系统由于其在实时帧速率下的深度传感能力而得到了广泛的应用。然而,由于TOF深度传感器的物理限制,与相应的彩色图像相比,捕获的深度图的分辨率较低。虽然已经提出了许多算法,但它们仍然会产生错误的结果,特别是当深度图和彩色图像的边界不对齐时。因此,我们提出了一种新的核回归框架来生成高质量的深度图。我们提出的滤波器是基于指向同质像素的向量,这些像素表示在局部区域中指向相似邻居的单位向量。这些向量用于检测颜色边缘和深度边缘之间的不对齐区域。通过与其他数据融合技术的实验比较,证明了该算法的优越性。
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