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

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 hig...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2016, Vol.8 (3), p.178-178
Main Authors: Gaiani, Marco, Remondino, Fabio, Apollonio, Fabrizio I, Ballabeni, Andrea
Format: Article
Language:English
Subjects:
3D reconstruction
Automation
denoise
enhancement
image matching
Image quality
Image reconstruction
Mathematical models
photogrammetry
Pipelines
pre-processing
Reconstruction
Remote sensing
Texture
Three dimensional models
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs8030178