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实景三维模型缺陷修复关键技术

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为了有效提高实景三维模型反映各类型地理要素的准确度和精细度以充分支撑实景三维中国建设工作,首先将当前无人机倾斜摄影三维重建后的三维模型中存在的模型缺陷系统性分为四大类型,并分析其产生的原因.随后,针对性地提出了三种关键技术:一是基于连通性和欧氏距离聚类的交互式悬浮物去除,利用图割理论和随机一致性抽样算法识别和删除悬浮物;二是基于图割理论的水面半自动整平,在利用Grabcut算法提取水面区域的同时为其赋予统一高程以实现水面的平整;三是基于深度网络模型的水体纹理inpainting填充,利用卷积神经网络和深度对抗训练以获得水体纹理的特征和结构从而实现水体缺失纹理的修复.最后,利用湖北省宜昌市的实际生产数据进行了修复实验,实验结果表明本文提出的各项关键技术能够有效修复三维模型缺陷,从而使实景三维模型具备更精细的地理实体和地理景观还原度.
Key Technologies for Repairing Defect of 3D Real Scene Model
In order to effectively improve the accuracy and precision of 3D real scene model reflecting various types of geographic features to fully support the 3D real scene construction in China,the model defects existing in the current 3D models reconstructed by UAV tilt photography into four types was classified and their causes was analyzed.Subsequently,three key technologies were proposed.First,interactive suspension removal based on connectivity and Euclidean distance clustering,using graph cut theory and random consistency sampling algorithm to identify and remove suspensions.Second,water surface semi-automatic leveling based on graph cut theory,using Grabcut algorithm to extract water surface area while giving it a uniform elevation to achieve water surface leveling.Third,water texture inpainting based on deep network model,using convolutional neural network and deep adversarial training to obtain the characteristics and structure of water texture to achieve repair of missing water texture.Finally,practical production data from Yichang City,Hubei Province was used for experiments.The experimental results showed that the key technologies proposed in this article could effectively repair 3D model defects,thereby enabling 3D real scene model to have more refined geographic entities and geographic landscape restoration.

oblique photogrammetry3D model defect repaircity-level 3DRStexture inpainting

龚元夫、熊忠招、陈关州、孙玉峰、廖溥昀、杨皓博、张晓东

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湖北省国土测绘院,武汉 430010

武汉大学,测绘遥感信息工程国家重点实验室,武汉 430079

倾斜摄影测量 模型缺陷修复 城市级实景三维 纹理填充

湖北省地质局科技项目湖北省地质局科技项目

KJ2020-33KJ2022-38

2024

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子:0.338
ISSN:1671-1815
年,卷(期):2024.24(19)
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