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

• 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
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs8030178

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.