Data-driven Reconstruction for Massive Buildings within Urban Scenarios: A Case Study

Chen, Ziye; Xiang, Xiaojia; Zhou, Han; Hu, Tianjiang

doi:10.1109/ddcls49620.2020.9275101

Cited by 3 publications

(1 citation statement)

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“…Data-driven methods and model-driven methods are the two main kinds of building modeling methods [10]. Between them, the data-driven method employs a bottom-up approach that begins with data extraction to reconstruct the geometric model [11,12]. This method has great flexibility and can reconstruct complex shapes; however, its success hinges highly on data accuracy.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Method of Connection Evaluation and Framework Optimization for Building Surface Reconstruction

Li,

Gong,

Liu

et al. 2024

Remote Sensing

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The three-dimensional (3D) reconstruction of buildings using photogrammetric point clouds is important for many applications, ranging from digital city construction to urban energy consumption analysis. However, problems such as building complexity and point cloud flaws may lead to incorrect modeling, which will affect subsequent steps such as texture mapping. This paper introduces a pipeline for building surface reconstruction from photogrammetric point clouds, employing a hybrid method that combines connection evaluation and framework optimization. Firstly, the plane segmentation method divides building point clouds into several pieces, which is complemented by a proposed candidate plane generation method aimed at removing redundancies and merging similarities. Secondly, the improved connection evaluation method detects potential skeleton lines from different planes. Subsequently, a framework optimization method is introduced to select suitable undirected polygonal boundaries from planes, forming the basis for plane primitives. Finally, by triangulating all plane primitives and filling holes, a building surface polygonal model is generated. Experiments conducted on various building examples provide both qualitative and quantitative evidence that the proposed hybrid method outperforms many existing methods, including traditional methods and deep learning methods. Notably, the proposed method successfully reconstructs the main building structures and intricate details, which can be further used to generate textural models and semantic models. Experimental results validate that the proposed method can be used for the surface reconstruction from photogrammetric point clouds of planar buildings.

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Section: Introductionmentioning

confidence: 99%