High Dynamic Range (HDR) content (i.e., images and videos) has a broad range
of applications. However, capturing HDR content from real-world scenes is
expensive and time- consuming. Therefore, the challenging task of
reconstructing visually accurate HDR images from their Low Dynamic Range (LDR)
counterparts is gaining attention in the vision research community. A major
challenge in this research problem is the lack of datasets, which capture
diverse scene conditions (e.g., lighting, shadows, weather, locations,
landscapes, objects, humans, buildings) and various image features (e.g.,
color, contrast, saturation, hue, luminance, brightness, radiance). To address
this gap, in this paper, we introduce GTA-HDR, a large-scale synthetic dataset
of photo-realistic HDR images sampled from the GTA-V video game. We perform
thorough evaluation of the proposed dataset, which demonstrates significant
qualitative and quantitative improvements of the state-of-the-art HDR image
reconstruction methods. Furthermore, we demonstrate the effectiveness of the
proposed dataset and its impact on additional computer vision tasks including
3D human pose estimation, human body part segmentation, and holistic scene
segmentation. The dataset, data collection pipeline, and evaluation code are
available at: this https URL

本文介绍了 GTA-HDR，一个大规模的合成高动态范围图像数据集，旨在解决从低动态范围图像准确重建出视觉逼真的 HDR 图像的问题，并证明了该数据集在 HDR 图像重建方法和其他计算机视觉任务上的有效性。