3D Point Cloud Registration using A-KAZE Features and Graph Optimization

• Published in: 2019 International Conference on Communication and Signal Processing (ICCSP) pp. 0898 - 0902
• Main Authors: Kumar N. C., Dayananda, Suresh, K.V., Dinesh, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: 3D Pose estimation; A-KAZE; Calibration; Cameras; Feature extraction; ICP; Optimization; Point cloud registration; Pose estimation; SVD; Three-dimensional displays; Two dimensional displays
• DOI: 10.1109/ICCSP.2019.8697917

The 3D reconstruction and modeling of face, objects and surrounding environment etc., has gained significant attention from the research community in recent days. Availability of depth data from various active and passive sensors have accelerated the work in 3D domain. It finds various applications in robotics, Human Computer Interaction (HCI), medical analysis etc. The major challenge in this area is accurate and real time 3D pose estimation which forms the basis for alignment of multi-view point cloud data. Noise in the depth data due to sensor irregularities significantly affects the accuracy of pose estimation. The main idea of this paper is to compute 2D A-KAZE feature correspondence and map to 3D for obtaining more reliable 3D sparse points. The 2D-3D correspondence is used to estimate accurate pose by formulating a graph optimization problem. The experimental results of this approach is evaluated against well known Iterative Closest Point (ICP) and Singular Value Decomposition (SVD) methods for point cloud registration on face and object data obtained using stereo camera. The results shows that the algorithm is able to estimate the accurate pose with least alignment error as compared with ICP and SVD methods.