GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要