一个有效的单幅图像深度估计算法

Baijiang Fan, Yunbo Rao, W. Liu, Jiali Song
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引用次数: 0

摘要

在许多情况下,图像深度估计是一项非常重要的工作。然而,传统的方法总是从双眼图像对中提取深度信息。由于单幅图像缺乏全局和局部坐标之间的关系,因此从单幅图像中估计深度信息要困难得多。提出了一种基于分割卷积神经网络的单幅图像深度估计方法。我们的方法旨在以高转速和高速度从单幅图像中获得深度图。该方法包括三个部分:分割、粗估计和高转数细化。实验结果表明,该方法可以获得高质量的结果。通过与其它方法在精度和处理时间上的比较,说明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective single image depth estimating algorithm
Depth estimating from image is a essentially important work in many situations. However, traditional methods always extract depth information from binocular image pairs. Estimating depth information from a single image is much harder because single image lake the relationship between global and local coordinate. This paper proposes a single image depth estimating method by the segmentation convolutional neural network method. Our method aimed at getting the depth map from a single image with high revolution and high speed. The proposed method include three components: segmentation, coarse estimating and high revolution refine. Experiment results show the method can get high quality results. We compare our method with other methods on the accuracy and processing time to show the advantages.
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