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Framework for automated reconstruction of 3D model from multiple 2D aerial images

机译:从多个2D航拍图像自动重建3D模型的框架

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The paper considers a problem of 3D environment model reconstruction from a set of 2D images acquired by the Unmanned Aerial Vehicle (UAV) in near real-time. The designed framework combines the FAST (Features from Accelerated Segment Test) algorithm and optical flow approach for detection of interest image points and adjacent images reconstruction. The robust estimation of camera locations is performed using the image points tracking. The coordinates of 3D points and the projection matrix are computed simultaneously using Structure-from-Motion (SfM) algorithm, from which the 3D model of environment is generated. The designed framework is tested using real image data and video sequences captured with camera mounted on the UAV. The effectiveness and quality of the proposed framework are verified through analyses of accuracy of the 3D model reconstruction and its time execution.
机译:本文从无人飞行器(UAV)几乎实时获取的一组2D图像中考虑了3D环境模型重构的问题。设计的框架结合了FAST(加速分段测试的特征)算法和光流方法,用于检测感兴趣的图像点和相邻图像重建。使用图像点跟踪执行摄像机位置的鲁棒估计。使用运动结构(SfM)算法同时计算3D点的坐标和投影矩阵,从而生成环境3D模型。使用安装在无人机上的摄像机捕获的真实图像数据和视频序列对设计的框架进行测试。通过分析3D模型重建的准确性及其时间执行,验证了所提出框架的有效性和质量。

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