Paper Info
Title
Extreme Feature Regions Detection And Accurate Quality Assessment For Point-Cloud 3d Reconstruction
Abstract
The 3-D reconstruction methods based on structure from motion (SfM) pipeline mainly use the traditional scale invariant feature detecting methods for feature matching. This type of methods suffers from the accuracy with affine matching in the image-based modeling system. In this paper, we propose an affine-invariant feature detection and matching method which is accurate and fast based on the three types of critical points in Morse theory called precise extreme feature region (PEFR). We also exploit the evaluation method for the reconstruction results without the ground truth. We propose the average descending rate to quantitatively evaluate the accuracy of the reconstruction model. Using PEFR in an SfM pipeline to build 3-D point-cloud, our method presents better results and less runtime compared to the state-of-the-art approaches. In addition, our quality assessment method presents a practical comparison of the reconstruction result based on the geometric accuracy of the reconstruction models. The experimental results prove that our PEFR method is faster and more accurate and works well on the reconstruction pipeline. The assessment method is fit for the quality assessment on 3-D reconstruction.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2898731
IEEE ACCESS
Keywords
Field
DocType
Affine invariant, image matching, accurate quality assessment, extreme feature regions, 3D reconstruction
Structure from motion,Affine transformation,Scale invariance,Computer science,Algorithm,Exploit,Ground truth,Point cloud,Morse theory,3D reconstruction,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yunbo Rao15412.25
Baijiang Fan200.34
Qifei Wang301.35
Jiansu Pu400.34
Xun Luo51410.67
Rize Jin62310.87