This paper introduces a new real and synthetic dataset called NeRFBK specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. High-quality 3D reconstruction has significant potential in various fields, and advancements in image-based algorithms make it essential to evaluate new advanced techniques. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK benchmark, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction.

翻译：本文介绍了一个名为NeRFBK的新型真实与合成数据集，该数据集专门用于测试和比较基于NeRF的三维重建算法。高质量的三维重建在各个领域具有重大潜力，而基于图像的算法进展使得评估新型先进技术变得至关重要。然而，收集具有精确真实数据的多样化数据集颇具挑战性，且可能无法涵盖所有相关应用场景。NeRFBK数据集通过提供多尺度、室内外的高分辨率图像与视频数据及相机参数，解决了这一问题，可用于测试和比较基于NeRF的算法。本文阐述了NeRFBK基准数据集的构建与设计过程，展示了多种示例及应用场景，并强调了其在推动三维重建领域发展方面的潜力。