论文标题
Holopix50k：一个大规模的野外立体声图像数据集
Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset
论文作者
论文摘要
随着双相机手机的大众市场采用，计算机视觉中利用立体声信息变得越来越重要。当前的最新方法利用基于学习的算法，其中培训样本的数量和质量严重影响了结果。现有的立体声图像数据集的大小或主题多样性受到限制。因此，在此类数据集中训练的算法并不能很好地推广到移动摄影中遇到的情况。我们介绍了Holopix50k，这是一种新颖的野外立体声图像数据集，包括49,368张图像对，由Holopix移动社交平台的用户贡献。在这项工作中，我们描述了我们的数据收集过程，并从统计上将数据集与其他流行的立体声数据集进行了比较。我们通过实验表明，使用我们的数据集可显着改善诸如立体声超分辨率和自我监管的单眼估计等任务的结果。最后，我们展示了数据集的实用应用，以激发新颖的作品和用例。 HOPIX50K数据集可从http://github.com/leiainc/holopix50k获得
With the mass-market adoption of dual-camera mobile phones, leveraging stereo information in computer vision has become increasingly important. 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 and self-supervised monocular depth estimation. Finally, we showcase practical applications of our dataset to motivate novel works and use cases. The Holopix50k dataset is available at http://github.com/leiainc/holopix50k
