Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset

PubDate: June 2020

Teams: Leia Inc

Writers: Yiwen Hua, Puneet Kohli, Pritish Uplavikar, Anand Ravi, Saravana Gunaseelan, Jason Orozco, Edward Li

PDF: Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset

Abstract

With the mass-market adoption of dual-camera mobile phones, leveraging stereo information in computer vision has become increasingly important in AR/VR industry. Current state-of-the-art methods utilize learning-based algorithms, where the amount and quality of training samples heavily influence results. Existing stereo image datasets are limited either in size or subject variety. Hence, algorithms trained on such datasets do not generalize well to scenarios encountered in mobile photography. We present Holopix50k, a novel in-the-wild stereo image dataset, comprising 49,368 image pairs contributed by users of the Holopix™ mobile social platform. In this work, we describe our data collection process and statistically compare our dataset to other popular stereo datasets. We experimentally show that using our dataset significantly improves results for tasks such as stereo super-resolution. Finally, we showcase practical applications of our dataset to train neural networks to predict disparity map from stereo and monocular images. The high-quality disparity maps are critical for improving the projection and 3D reconstruction for AR/VR applications on mobile phones.