Performance analysis of interest point detection/matching on shiny and non-textured surfaces

Abstract

3D modeling techniques can be used to automate processes such as damage assessment in aircraft engines. Aircraft engines often have shiny and non-textured surfaces, where these modeling techniques often have poor performance. This paper gives more insight into the performance of interest detection/matching algorithms on shiny and non-textured surfaces as found in aircraft engine borescope inspection videos. These algorithms are often used in 3D modeling techniques. Three interest point detection/matching algorithms are executed on different test videos, and various metrics are calculated for each algorithm. This paper is the first paper that compares both recent and traditional computer vision interest point detection/matching algorithms in these specific settings, and contributes to a better understanding of the usability of feature-based 3D reconstruction techniques. The results show that more recent neural network-based approaches outperform traditional approaches.