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Remote Sens. 2016, 8(3), 178; doi:10.3390/rs8030178

An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes

1
Department of Architecture, University of Bologna, Bologna 40136, Italy
2
3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento 38123, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Diego Gonzalez-Aguilera, Norman Kerle and Prasad S. Thenkabail
Received: 1 December 2015 / Revised: 17 January 2016 / Accepted: 25 January 2016 / Published: 25 February 2016
View Full-Text   |   Download PDF [20919 KB, uploaded 25 February 2016]   |  

Abstract

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. View Full-Text
Keywords: pre-processing; denoise; enhancement; 3D reconstruction; photogrammetry; image matching; automation pre-processing; denoise; enhancement; 3D reconstruction; photogrammetry; image matching; automation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gaiani, M.; Remondino, F.; Apollonio, F.I.; Ballabeni, A. An Advanced Pre-Processing Pipeline to Improve Automated Photogrammetric Reconstructions of Architectural Scenes. Remote Sens. 2016, 8, 178.

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