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.

翻译：随着低成本数据获取方式（如单图像到三维重建）的发展，三维建筑生成日益重要。然而，现有基于单图像的三维建筑生成方法大多局限于特定视角图像，难以推广至实际场景中常见的通用视角图像。为填补这一空白，我们提出了一种新颖的三维建筑形状生成方法，该方法利用结合图像条件机制的点云扩散模型，展现出对输入图像的灵活适应性。通过协同两个条件扩散模型并在去噪过程中引入正则化策略，我们的方法能够在保持整体结构的同时合成建筑屋顶。我们在两个新构建的数据集上验证了框架有效性，大量实验表明，该方法在建筑生成质量方面优于现有工作。