Image-based 3D Building Reconstruction Using A-KAZE Feature Extraction Algorithm

Abstract

The development of 3D reconstruction from 2D building images enables cost-effective and accurate acquisition of spatial data. Feature extraction is a fundamental technique for 3D reconstruction method. However, buildings mostly consist of planar surfaces whose entities are featureless. This study presents 3D building reconstruction using A-KAZE feature extraction algorithm. Because A-KAZE algorithm does not use Gaussian blurring like SIFT and SURF, A-KAZE algorithm has potential to extract correct visual features for feature matching and 3D reconstruction. The proposed method was tested on actual building scenes acquired from a high-resolution camera. The experimental results showed that the A-KAZE algorithm can detect the sufficient number of features with low computation time. It is expected that the proposed method can be implemented in comprehensive 3D reconstruction of civil infrastructures.

Real-time currency recognition on video using AKAZE algorithm

“…The second stage is constructing a nonlinear scalespace [15]. The scale-space approach is as same as the SIFT algorithm, which discretizes the scale-space in logarithmic steps arranged in octaves and scales.…”