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：本文提出了一种名为NeRFBK的新型真实与合成数据集，专门用于测试和比较基于NeRF的三维重建算法。高质量三维重建在多个领域具有重要应用潜力，而基于图像算法的技术进步使得评估新兴先进技术变得至关重要。然而，收集具有精确真实标注的多样化数据具有挑战性，且可能无法覆盖所有相关应用场景。NeRFBK数据集通过提供多尺度、室内外场景的高分辨率图像与视频数据及相机参数，有效解决了这一问题，可用于测试和比较基于NeRF的算法。本文详细阐述了NeRFBK基准数据集的设计与构建过程，展示了多种示例与应用场景，并凸显了其在推动三维重建领域发展方面的潜力。