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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.|
|Citation:||Chen, Weifeng, Shengyi Qian, and Jia Deng. "Learning Single-Image Depth From Videos Using Quality Assessment Networks." IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019): pp. 5597-5606. doi:10.1109/CVPR.2019.00575|
|Pages:||5597 - 5606|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)|
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