Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras
Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras
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DOI：
10.1109/cvpr.2013.157
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Ju Shen;S. Cheung
中科院分区：
文献类型：
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作者：
Ju Shen;S. Cheung
结构光深度传感器最近的流行使得从基于手势的用户界面到3D重建的许多新应用成为可能。然而，这些系统的深度测量质量远非完美。某些深度值可能存在重大错误，而其他深度值可能完全缺失。这些传感器之间的深度测量的不确定性会显著降低任何后续视觉处理的性能。在本文中，我们提出了一种新的概率模型，以捕捉各种类型的不确定性，在深度测量过程中的结构光系统。我们模型的关键是使用深度层来解释前景对象和背景场景之间的差异，缺失深度值现象以及颜色和深度通道之间的相关性。深度层标记作为最大后验估计问题来解决，并且与测量中的不确定性相协调的马尔可夫随机场用于在空间上平滑标记过程。使用深度层标签，我们提出了一个深度校正和完成算法，优于文献中的其他技术。
The recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. The quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. The uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. The key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. The depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.