Authors:
Samuel Macedo
;
Luis Vasconcelos
;
Vinicius Cesar
;
Saulo Pessoa
and
Judith Kelner
Affiliation:
GRVM, Brazil
Keyword(s):
Outlier Detection, Optical Flow, Computer Vision, 3D Reconstruction, Statistical Inference.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Pattern Recognition
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
The 3D reconstruction can be employed in several areas such as markerless augmented reality, manipulation of interactive virtual objects and to deal with the occlusion of virtual objects by real ones. However, many improvements into the 3D reconstruction pipeline in order to increase its efficiency may still be done. In such context, this paper proposes a filter for optimizing a 3D reconstruction pipeline. It is presented the SKen technique, a statistical hypothesis test that classifies the features by checking the smoothness of its trajectory. Although it was not mathematically proven that inliers features performed smooth camera paths, this work shows some evidence of a relationship between smoothness and inliers. By removing features that did not present smooth paths, the quality of the 3D reconstruction was enhanced.