AIM2PC: Aerial Image to 3D Building Point Cloud Reconstruction

• URL: http://arxiv.org/abs/2503.18527v2
• Date: Tue, 25 Mar 2025 09:44:41 GMT
• Title: AIM2PC: Aerial Image to 3D Building Point Cloud Reconstruction
• Authors: Soulaimene Turki, Daniel Panangian, Houda Chaabouni-Chouayakh, Ksenia Bittner,
• Abstract summary: Recent methods primarily focus on rooftops from aerial images, often overlooking essential geometrical details.<n>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.<n>This paper presents a novel methodology, AIM2PC, which utilizes our generated dataset that includes complete 3D point clouds determined camera poses.
• Score: 2.9998889086656586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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