Single-Image Depth Perception in the Wild
Single-Image Depth Perception in the Wild
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发表时间：
2016-04
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通讯作者：
Weifeng Chen;Z. Fu;Dawei Yang;Jia Deng
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作者：
Weifeng Chen;Z. Fu;Dawei Yang;Jia Deng
本文研究了野外单幅图像的深度感知，即从无约束环境下拍摄的单幅图像中恢复深度。我们引入了一个新的数据集“Depth in the Wild”，它由野外的图像组成，这些图像在两对随机点之间用相对深度进行标注。我们还提出了一种新的算法，该算法学习使用相对深度的标注来估计度量深度。与现有技术相比，我们的算法更简单，性能更好。实验表明，我们的算法结合现有的RGB-D数据和我们新的相对深度标注，显著改善了野外单幅图像的深度感知。
This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.