Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery

We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables...

Full description

Saved in:
Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Khomiakov, Maxim, Mahou, Alejandro Valverde, Sanchez, Alba Reinders, Frellsen, Jes, Andersen, Michael Riis
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
3D Reconstruction
aerial imagery
Buildings
generative models
Image resolution
remote sensing
Sensitivity
Signal processing
Solid modeling
Three-dimensional displays
Transformers
Online AccessGet full text

Cover

More Information
Summary:We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for predictions. The approach employs the PolyGen, a deep generative transformer architecture for 3D meshes. We apply this model in a new domain and investigate the sensitivity of the image resolution. We propose a novel metric to evaluate the performance of the inferred meshes, and our results show that the model is robust even at lower resolutions, while qualitatively producing realistic representations for out-of-distribution samples.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095974