从单张航拍图重建完整建筑点云，包含墙体细节。
AIM2PC: Aerial Image to 3D Building Point Cloud Reconstruction
- 融合图像特征与边缘、掩码等条件，提升重建精度。
- 基于扩散模型的点云生成方法，实现完整建筑重建。
- 首次公开含完整点云和相机位姿的航拍数据集。
过去二十年来，仅从单张图像重建三维城市建筑受到广泛关注。然而，现有方法多聚焦于屋顶，常忽略关键几何细节。此外，缺乏包含完整建筑3D点云的数据集，且航拍图像相机位姿难以准确获取。本文提出新型方法AIM2PC，利用自建数据集（含完整3D点云与相机位姿），将单张航拍图像特征与二值掩码、Sobel边缘图等附加条件拼接输入，实现更注重边缘的重建。通过基于中心化去噪扩散概率模型（CDPM）的点云扩散模型，结合每一步的相机位姿将特征投影至部分去噪点云。所提方法可完整重建建筑点云，包含墙面信息，在基准方法上表现更优。相关数据集已公开于https://github.com/Soulaimene/AIM2PCDataset。
原文摘要 · Abstract (English)
Three-dimensional urban reconstruction of buildings from single-view images has attracted significant attention over the past two decades. However, recent methods primarily focus on rooftops from aerial images, often overlooking essential geometrical details. Additionally, there is a notable lack of datasets containing complete 3D point clouds for entire buildings, along with challenges in obtaining reliable camera pose information for aerial images. This paper addresses these challenges by presenting a novel methodology, AIM2PC , which utilizes our generated dataset that includes complete 3D point clouds and determined camera poses. Our approach takes features from a single aerial image as input and concatenates them with essential additional conditions, such as binary masks and Sobel edge maps, to enable more edge-aware reconstruction. By incorporating a point cloud diffusion model based on Centered denoising Diffusion Probabilistic Models (CDPM), we project these concatenated features onto the partially denoised point cloud using our camera poses at each diffusion step. The proposed method is able to reconstruct the complete 3D building point cloud, including wall information and demonstrates superior performance compared to existing baseline techniques. To allow further comparisons with our methodology the dataset has been made available at https://github.com/Soulaimene/AIM2PCDataset
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