SYNTHETIC DATA GENERATION PIPELINE FOR GEOMETRIC DEEP LEARNING IN ARCHITECTURE

Autor: S. Fedorova, A. Tono, M. S. Nigam, J. Zhang, A. Ahmadnia, C. Bolognesi, D. Michels

Publikováno v: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2021, Pp 337-344 (2021)

With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. Due to the complexity of such 3D datasets, the most widespread tec

Externí odkaz: https://doaj.org/article/cff0beb30d344b41adcaf2e8cfd1c577