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Article

Evaluating Feature Extraction Methods with Synthetic Noise Patterns for Image-Based Modelling of Texture-Less Objects

1
Department of Plasma Bio Display, Kwangwoon University, Seoul 01897, Korea
2
Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Korea
3
Spatial Computing Convergence Center, Kwangwoon University, Seoul 01897, Korea
4
Department of Digital Contents, Dongshin University, Naju-si 58245, Korea
5
Ingenium College, Kwangwoon University, Seoul 01897, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(23), 3886; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233886
Received: 20 October 2020 / Revised: 18 November 2020 / Accepted: 24 November 2020 / Published: 27 November 2020
(This article belongs to the Special Issue 3D Virtual Reconstruction for Cultural Heritage)
Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively. View Full-Text
Keywords: structure-from-motion; multi-view stereovision; textures-less photogrammetry; pattern recognition; 3D surface comparison structure-from-motion; multi-view stereovision; textures-less photogrammetry; pattern recognition; 3D surface comparison
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MDPI and ACS Style

Hafeez, J.; Lee, J.; Kwon, S.; Ha, S.; Hur, G.; Lee, S. Evaluating Feature Extraction Methods with Synthetic Noise Patterns for Image-Based Modelling of Texture-Less Objects. Remote Sens. 2020, 12, 3886. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233886

AMA Style

Hafeez J, Lee J, Kwon S, Ha S, Hur G, Lee S. Evaluating Feature Extraction Methods with Synthetic Noise Patterns for Image-Based Modelling of Texture-Less Objects. Remote Sensing. 2020; 12(23):3886. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233886

Chicago/Turabian Style

Hafeez, Jahanzeb, Jaehyun Lee, Soonchul Kwon, Sungjae Ha, Gitaek Hur, and Seunghyun Lee. 2020. "Evaluating Feature Extraction Methods with Synthetic Noise Patterns for Image-Based Modelling of Texture-Less Objects" Remote Sensing 12, no. 23: 3886. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233886

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