Paper Info
Title
Computer Vision Meets Geometric Modeling: Multi-view Reconstruction of Surface Points and Normals Using Affine Correspondences.
Abstract
A novel surface normal estimator is introduced using affine-invariant features extracted and tracked across multiple views. Normal estimation is robustified and integrated into our reconstruction pipeline that has increased accuracy compared to the State-of-the-Art. Parameters of the views and the obtained spatial model, including surface normals, are refined by a novel bundle adjustment-like numerical optimization. The process is an alternation with a novel robust view-dependent consistency check for surface normals, removing normals inconsistent with the multiple-view track. Our algorithms are quantitatively validated on the reverse engineering of geometrical elements such as planes, spheres, or cylinders. It is shown here that the accuracy of the estimated surface properties is appropriate for object detection. The pipeline is also tested on the reconstruction of man-made and free-form objects.
Year
Venue
Field
2017
ICCV Workshops
Iterative reconstruction,Affine transformation,Surface reconstruction,Computer vision,Affine shape adaptation,Object detection,Harris affine region detector,Computer science,Geometric modeling,Artificial intelligence,Normal
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Levente Hajder14312.55
Ivan Eichhardt210.68