三維建物線框模型之無人機影像自動重建

Automatic UAV Image Reconstruction for 3D Building Wireframe Models

10.6574/JPRS.202409_29(3).0001

黃郁翎(Yu-Ling Huang)；莊子毅(Tzu-Yi Chuang)

多視角無人機影像 ； 影像建模 ； 自動化建物線框重建 ； 線框模型 ； 深度學習 ； Multi-View UAV Imagery ； Image Modeling ； Automated Building Wireframe Reconstruction ； Wireframe Models ； Deep Learning

航測及遙測學刊

29卷3期（2024 / 09 / 01）

129 - 149

繁體中文;英文

三維建物圖資在智慧城市規劃、管理和能源評估中扮演著重要角色。然而，由於作業繁瑣且自動化不足，針對既有建物構建精確的三維模型依然充滿挑戰。本研究提出基於多視角無人機影像的演算策略，生成具備側向幾何細節的三維線框模型，可做為進行既存建物三維房屋模型建置之基礎，提升作業效率並降低成本。演算程序運用預訓練的角點檢測模型及提出的角點萃取演算，採用「由粗到細」的策略實現角點定位。同時，運用虛擬角點重建策略來降低都市UAV影像中無可避免的遮蔽與數據缺失影響。實驗結果顯示，演算策略可適應具曲線形之建築結構，建築角點平均精度約為30 cm，並可達到98%的線框重建完整度。

3D building data is vital in smart city planning, management, and energy assessment. However, constructing accurate 3D models for existing buildings remains challenging due to the labor-intensive processes and insufficient automation. This study proposes an algorithmic strategy based on multi-view UAV imagery to generate 3D wireframe models with detailed lateral geometric features, serving as a foundation for constructing 3D building models of existing structures. This approach aims to improve operational efficiency and reduce costs. The algorithm employs a pre-trained corner detection model and a novel corner extraction algorithm, utilizing a "coarse-to-fine" strategy to achieve precise corner localization. Additionally, a virtual corner reconstruction strategy is employed to mitigate the inevitable occlusion and data loss in urban UAV imagery. Experimental results demonstrate that this algorithmic strategy adapts well to buildings with curved architectural structures, achieving an average corner localization accuracy of approximately 30 cm and up to 98% completeness in wireframe reconstruction.