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: https://github.com/HrishavBakulBarua/GTA-HDR.

翻译：高动态范围（HDR）内容（即图像和视频）具有广泛的应用场景。然而，从真实世界场景中采集HDR内容成本高昂且耗时。因此，从低动态范围（LDR）对应图像中重建视觉准确的HDR图像这一具有挑战性的任务，正日益受到视觉研究领域的关注。该研究问题的主要挑战之一是缺乏能够涵盖多样化场景条件（如光照、阴影、天气、地点、景观、物体、人物、建筑）及多种图像特征（如色彩、对比度、饱和度、色调、亮度、明度、辐射度）的数据集。为弥补这一不足，本文提出了GTA-HDR——一个从GTA-V视频游戏中采样的、包含逼真HDR图像的大规模合成数据集。我们对所提出的数据集进行了全面评估，结果表明其能够显著提升现有最优HDR图像重建方法的定性与定量性能。此外，我们进一步论证了该数据集的有效性及其在三维人体姿态估计、人体部位分割、整体场景分割等计算机视觉任务中的积极作用。该数据集、数据采集流程及评估代码已开源至：https://github.com/HrishavBakulBarua/GTA-HDR。