{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T13:16:43Z","timestamp":1769260603843,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,2,25]],"date-time":"2016-02-25T00:00:00Z","timestamp":1456358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automated image-based 3D reconstruction methods are more and more flooding our 3D modeling applications. Fully automated solutions give the impression that from a sample of randomly acquired images we can derive quite impressive visual 3D models. Although the level of automation is reaching very high standards, image quality is a fundamental pre-requisite to produce successful and photo-realistic 3D products, in particular when dealing with large datasets of images. This article presents an efficient pipeline based on color enhancement, image denoising, color-to-gray conversion and image content enrichment. The pipeline stems from an analysis of various state-of-the-art algorithms and aims to adjust the most promising methods, giving solutions to typical failure causes. The assessment evaluation proves how an effective image pre-processing, which considers the entire image dataset, can improve the automated orientation procedure and dense 3D point cloud reconstruction, even in the case of poor texture scenarios.<\/jats:p>","DOI":"10.3390\/rs8030178","type":"journal-article","created":{"date-parts":[[2016,2,25]],"date-time":"2016-02-25T10:24:25Z","timestamp":1456395865000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1674-0874","authenticated-orcid":false,"given":"Marco","family":"Gaiani","sequence":"first","affiliation":[{"name":"Department of Architecture, University of Bologna, Bologna 40136, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-5342","authenticated-orcid":false,"given":"Fabio","family":"Remondino","sequence":"additional","affiliation":[{"name":"3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento 38123, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5186-1378","authenticated-orcid":false,"given":"Fabrizio","family":"Apollonio","sequence":"additional","affiliation":[{"name":"Department of Architecture, University of Bologna, Bologna 40136, Italy"}]},{"given":"Andrea","family":"Ballabeni","sequence":"additional","affiliation":[{"name":"Department of Architecture, University of Bologna, Bologna 40136, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2016,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11263-007-0107-3","article-title":"Modeling the world from internet photo collections","volume":"80","author":"Snavely","year":"2008","journal-title":"Int. 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