Multiview stereopsis (MVS) methods, which can reconstruct both the 3D geometry and texture from multiple images, have been rapidly developed and extensively investigated from the feature engineering methods to the data-driven ones. However, there is no dataset containing both the 3D geometry of large-scale scenes and high-resolution observations of small details to benchmark the algorithms. To this end, we present GigaMVS, the first gigapixel-image-based 3D reconstruction benchmark for ultra-large-scale scenes. The gigapixel images, with both wide field-of-view and high-resolution details, can clearly observe both the Palace-scale scene structure and Relievo-scale local details. The ground-truth geometry is captured by the laser scanner, which covers ultra-large-scale scenes with an average area of 8667 m2^2 and a maximum area of 32007 m2^2. Owing to the extremely large scale, complex occlusion, and gigapixel-level images, GigaMVS exposes problems that emerge from the poor scalability and efficiency of the existing MVS algorithms. We thoroughly investigate the state-of-the-art methods in terms of geometric and textural measurements, which point to the weakness of the existing methods and promising opportunities for future works. We believe that GigaMVS can benefit the community of 3D reconstruction and support the development of novel algorithms balancing robustness, scalability and accuracy.

中文翻译：

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GigaMVS：超大规模千兆像素级 3D 重建的基准


多视图立体视觉 (MVS) 方法可以从多个图像重建 3D 几何和纹理，从特征工程方法到数据驱动方法，得到了快速发展和广泛研究。然而，没有既包含大型场景的 3D 几何图形又包含小细节的高分辨率观察的数据集来对算法进行基准测试。为此，我们推出了 GigaMVS，这是第一个基于十亿像素图像的超大规模场景 3D 重建基准。十亿像素图像兼具宽视场和高分辨率细节，可清晰观察宫殿规模的场景结构和浮雕规模的局部细节。地面实况几何由激光扫描仪捕获，覆盖平均面积为8667 m2^2、最大面积为32007 m2^2的超大规模场景。由于图像规模极大、遮挡复杂、千兆像素级，GigaMVS暴露了现有MVS算法可扩展性和效率差的问题。我们深入研究了几何和纹理测量方面最先进的方法，这指出了现有方法的弱点和未来工作的有希望的机会。我们相信 GigaMVS 可以使 3D 重建社区受益，并支持平衡鲁棒性、可扩展性和准确性的新颖算法的开发。