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Guided inpainting and filtering for Kinect depth maps

机译:Kinect深度图的引导式修补和过滤

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Depth maps captured by Kinect-like cameras are lack of depth data in some areas and suffer from heavy noise. These defects have negative impacts on practical applications. In order to enhance the depth maps, this paper proposes a new inpainting algorithm that extends the original fast marching method (FMM) to reconstruct unknown regions. The extended FMM incorporates an aligned color image as the guidance for inpainting. An edge-preserving guided filter is further applied for noise reduction. To validate our algorithm and compare it with other existing methods, we perform experiments on both the Kinect data and the Middlebury dataset which, respectively, provide qualitative and quantitative results. The results show that our method is efficient and superior to others.
机译:用类似Kinect的相机捕获的深度图在某些区域缺少深度数据,并且受到严重干扰。这些缺陷会对实际应用产生负面影响。为了增强深度图,本文提出了一种新的修复算法,该算法扩展了原有的快速行进方法(FMM)来重建未知区域。扩展的FMM包含对齐的彩色图像作为修补指导。保留边缘的导引滤波器进一步用于降低噪声。为了验证我们的算法并将其与其他现有方法进行比较,我们分别对Kinect数据和Middlebury数据集进行了实验,分别提供了定性和定量结果。结果表明,我们的方法是有效的并且优于其他方法。

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