BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models

Autor:	Wei, Yao, Vosselman, George, Yang, Michael Ying
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	3D building generation with low data acquisition costs, such as single image-to-3D, becomes increasingly important. However, most of the existing single image-to-3D building creation works are restricted to those images with specific viewing angles, hence they are difficult to scale to general-view images that commonly appear in practical cases. To fill this gap, we propose a novel 3D building shape generation method exploiting point cloud diffusion models with image conditioning schemes, which demonstrates flexibility to the input images. By cooperating two conditional diffusion models and introducing a regularization strategy during denoising process, our method is able to synthesize building roofs while maintaining the overall structures. We validate our framework on two newly built datasets and extensive experiments show that our method outperforms previous works in terms of building generation quality. Comment: 10 pages, 6 figures, accepted to ICCVW2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.00158 Zobrazit plný text záznamu View this record from Arxiv