SYNTHETIC DATA GENERATION PIPELINE FOR GEOMETRIC DEEP LEARNING IN ARCHITECTURE

Autor: Amirhossein Ahmadnia, Jiayao Zhang, Cecilia Maria Bolognesi, Meher Shashwat Nigam, Dominik L. Michels, Alberto Tono, Stanislava Fedorova

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://explore.openaire.eu/search/publication?articleId=doi_dedup___::222129bdb6a79e43734a6da7e11e6070
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/337/2021/isprs-archives-XLIII-B2-2021-337-2021.pdf