GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction

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 is the lack of datasets which capture diverse scene conditions (e.g. lighting weather locations) and various image features (e.g. color contrast saturation). To address this gap 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 enables 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.