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arXiv · 1806.09573

Learning Single-Image Depth from Videos using Quality Assessment Networks

Weifeng Chen·Shengyi Qian·Jia Deng
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Abstract

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild.

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Weifeng Chen, Shengyi Qian, Jia Deng. 2018-06-25. Learning Single-Image Depth from Videos using Quality Assessment Networks. https://arxiv.org/abs/1806.09573

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