Qu Yufu, Huang Jianyu, Zhang Xuan
Department of Measurement Technology & Instrument, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Jan 14;18(1):225. doi: 10.3390/s18010225.
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. 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. 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. Furthermore, as the number of images increases, the improvement in the calculation speed will become more noticeable.
为了从无人机相机（UAV）拍摄的图像序列中重建三维（3D）结构并提高处理速度，考虑到无人机相机图像的连续性和相关性，我们提出了一种基于图像队列的快速3D重建方法。该方法首先使用主成分分析方法将每张图像的特征点压缩为三个主成分点。为了选择适合3D重建的关键图像，利用主成分点估计图像之间的相互关系。其次，将这些关键图像插入到固定长度的图像队列中。计算图像的位置和方向，并使用加权束调整估计特征点的3D坐标。利用这些结构信息，可以计算出这些图像的深度图。接下来，通过删除一些旧图像并将一些新图像插入队列来更新图像队列，重复上述步骤可以对所有图像进行结构计算。最后，使用深度图融合方法可以获得密集的3D点云。实验结果表明，当图像纹理复杂且图像数量超过100时，该方法在几乎不损失精度的情况下可以将计算速度提高四倍以上。此外，随着图像数量的增加，计算速度的提升将更加显著。
