Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery

• URL: http://arxiv.org/abs/2303.11215v1
• Date: Mon, 20 Mar 2023 15:47:05 GMT
• Title: Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery
• Authors: Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders S\'anchez, Jes Frellsen, Michael Riis Andersen
• Abstract summary: We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image. 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.
• Score: 68.3565370706598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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