{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T06:17:32Z","timestamp":1780813052235,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,14]],"date-time":"2018-01-14T00:00:00Z","timestamp":1515888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to reconstruct three-dimensional (3D) structures from an image sequence captured by unmanned aerial vehicles\u2019 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\u2013map 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.<\/jats:p>","DOI":"10.3390\/s18010225","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T12:30:36Z","timestamp":1516019436000},"page":"225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera"],"prefix":"10.3390","volume":"18","author":[{"given":"Yufu","family":"Qu","sequence":"first","affiliation":[{"name":"Department of Measurement Technology &amp; Instrument, School of Instrumentation Science &amp; Optoelectronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Measurement Technology &amp; Instrument, School of Instrumentation Science &amp; Optoelectronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Measurement Technology &amp; Instrument, School of Instrumentation Science &amp; Optoelectronics Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,14]]},"reference":[{"key":"ref_1","first-page":"172","article-title":"Incremental Block Cholesky Factorization for Nonlinear Least Squares in Robotics","volume":"46","author":"Polok","year":"2013","journal-title":"Robot. 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