构建1000对真实场景反射去除图像数据集，解决现有数据不足问题。
OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal
- 从真实环境采集高质量成对图像，保证像素级对齐
- 包含1000对自然多样的传输-反射图像对，覆盖复杂场景
- 适合做反射去除算法训练与评估，尤其提升实际应用鲁棒性
反射去除技术在摄影和计算机视觉中至关重要，但现有方法受限于缺乏高质量的真实世界数据集。本文提出一种新颖的采集范式，从全新视角收集反射数据。该方法简便、低成本且可扩展，确保所获数据对质量高、完全对齐，并反映自然多样的真实场景。基于此范式，我们构建了名为OpenRR-1k的数据集，包含1,000对在真实环境中采集的高质量传输-反射图像对。通过对多种反射去除方法的分析及在该数据集上的基准评估实验，验证了其在提升复杂真实环境下的鲁棒性方面的有效性。数据集已开源：https://github.com/caijie0620/OpenRR-1k。
原文摘要 · Abstract (English)
Reflection removal technology plays a crucial role in photography and computer vision applications. However, existing techniques are hindered by the lack of high-quality in-the-wild datasets. In this paper, we propose a novel paradigm for collecting reflection datasets from a fresh perspective. Our approach is convenient, cost-effective, and scalable, while ensuring that the collected data pairs are of high quality, perfectly aligned, and represent natural and diverse scenarios. Following this paradigm, we collect a Real-world, Diverse, and Pixel-aligned dataset (named OpenRR-1k dataset), which contains 1,000 high-quality transmission-reflection image pairs collected in the wild. Through the analysis of several reflection removal methods and benchmark evaluation experiments on our dataset, we demonstrate its effectiveness in improving robustness in challenging real-world environments. Our dataset is available at https://github.com/caijie0620/OpenRR-1k.
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