原文标题：Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera
Yufu Qu *, Jianyu Huang and Xuan Zhang
Department of Measurement Technology & Instrument, School of Instrumentation Science & Optoelectronics
Engineering, Beihang University, Beijing 100191, China; Hjy448@buaa.edu.cn (J.H.);
Correspondence: email@example.com; Tel.: +86-010-8231-7336
Received: 23 November 2017; Accepted: 11 January 2018; Published: 14 January 2018
In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles’ camera (UAVs) and improve the processing speed, we propose a rapid 3D reconstruction method that is based on an image queue, considering the continuity and relevance of UAV camera images.
The proposed approach first compresses the feature points of each image into three principal component points by using the principal component analysis method. In order to select the key images suitable for 3D reconstruction, the principal component points are used to estimate the interrelationships between images.
该方法首先利用主成分分析法(Principal Components Analysis, PCA)将每幅图像的特征点压缩为 3 个主成分点。利用主成分点估计图像之间的相互关系，可以为选择适合三维重建的关键帧提供依据。
Second, these key images are inserted into a fixed-length image queue. The positions and orientations of the images are calculated, and the 3D coordinates of the feature points are estimated using weighted bundle adjustment. With this structural information, the depth maps of these images can be calculated.
其次，将这些关键帧插入到一个固定长度的图像队列中。计算图像的位置和方向，并使用加权束平差(weighted bundle adjustment)估计特征点的三维坐标。利用这些结构信息，可以计算出这些图像的深度图。
Next, we update the image queue by deleting some of the old images and inserting some new images into the queue, and a structural calculation of all the images can be performed by repeating the previous steps.
Finally, a dense 3D point cloud can be obtained using the depth–map fusion method. The experimental results indicate that when the texture of the images is complex and the number of images exceeds 100, the proposed method can improve the calculation speed by more than a factor of four with almost no loss of precision.
最后利用深度图融合方法得到密集的三维点云。实验结果表明，当图像的纹理复杂且图像数量超过 100 时，该方法可以将计算速度提高 4 倍以上，几乎不损失精度。
Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.
Keywords: UAV camera; multi-view stereo; structure from motion; 3D reconstruction; point cloud