Learning Single-Image Depth from Videos using Quality Assessment Networks
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Although significant progress has been made in recent years, depth estimation from a single image in the wild is still a very challenging problem. One reason is the lack of high-quality image-depth data in the wild. In this paper we propose a fully automatic pipeline based on Structure-from-Motion (SfM) to generate such data from arbitrary videos. The core of this pipeline is a Quality Assessment Network that can distinguish correct and incorrect reconstructions obtained from SfM. With the proposed pipeline, we generate image-depth data from the NYU Depth dataset and random YouTube videos. We show that depth-prediction networks trained on such data can achieve competitive performance on the NYU Depth and the Depth-in-the-Wild benchmarks.
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