Example：10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
GigaMVS: A Benchmark for Ultra-large-scale Gigapixel-level 3D Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence (IF16.389), Pub Date : 2021-09-24, DOI: 10.1109/tpami.2021.3115028 Jianing Zhang,Jinzhi Zhang,Shi Mao,Mengqi Ji,Guangyu Wang,Zequn Chen,Tian Zhang,Xiaoyun Yuan,Qionghai Dai,Lu Fang
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 m^2 and a maximum area of 32007 m^2. Due to the extremely large scale, complex occlusion, and gigapixel-level images, GigaMVS brings the problem to light that emerged from the poor effectiveness 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 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.