ORTHOIMAGE-TO-2D ARCHITECTURAL DRAWING WITH CONDITIONAL ADVERSARIAL NETWORKS

Abstract

Vectorization of orthoimages of Cultural Heritage sites requires a considerable amount of time and constant supervision by qualified professionals. In addition, this 2D architectural drawing creation requires expert knowledge for appropriate interpretation of the orthoimages. In this paper, the use of conditional adversarial networks as a solution to orthoimage-to-drawing translation problems is proposed. The presented work exploits a state of the art conditional Generative Adversarial Network with a Markovian discriminator and modifies it using a ResNet fully convolutional network as generator in order to deliver reliable and accurate 2D architectural drawings in a binary image format. Following the 2D drawing image generation, their automated conversion into vector files is performed through a vectorization function, giving also the possibility to edit and scale the edges. Experimental results over two different Cultural Heritage test sites demonstrates that this approach is highly effective at synthesising 2D architectural drawings from orthoimages in great detail and reliability by learning the interpretation performed by the expert architects during the vectorization process.