Intelligent auxiliary design of subway station building space based on deep learning

Abstract

This paper aims to study the generation of spatial function layout of subway stations assisted by deep learning, and train the point cloud data of subway stations based on the Pointnet + + model in deep learning. The point cloud data of subway stations comes from the subway station data of large and medium-sized cities collected and processed by the author. After training and verification, the following conclusions are drawn: (1) It has been verified that the spatial deep learning of buildings in the form of point clouds is highly feasible, and the point cloud format of the architectural space model is fully compatible under the Pointnet + + model. (2) Verified the effectiveness of Pointnet + + for semantic segmentation and prediction of subway station cloud information. The results show that the predicted data has 60%+MIOU (MeanIoU average intersection and union ratio) and 75%+Acc (Accuracy). This paper uses an interdisciplinary research method to combine deep learning of 3D point cloud data with architectural design, breaking through the current situation of using 2D images as research objects, and avoiding the application of "deep learning" to 2D images. Objects cannot accurately describe the limitations of 3D space, providing architects with more intuitive and diverse design assistance.