3D Object Recognition with Keypoint Based Algorithms
Journal Title: International Journal of Environment and Geoinformatics - Year 2019, Vol 6, Issue 1
Abstract
Object recognition is important in many practical applications of computer vision. Traditional 2D methods are negatively affected by illumination, shadowing and viewpoint. 3D methods have the potential to solve these problems, because 3D models include geometric properties of the objects. In this paper, 3D local feature based algorithms were used for 3D object recognition. The local feature was keypoint. This study aimed to research facilities of keypoints for 3D object recognition. Keypoint is feature of object that is detected by detector algorithms according to certain mathematical base. A recognition system was designed. For this purpose, a database that includes 3D model of objects was created. The algorithms were improved in MATLAB. The keypoints on the 3D models were detected using keypoint detectors. These keypoints were described by keypoints descriptors. The descriptor algorithms detect geometrical relation between each point of point cloud and create a histogram. In the third step, the keypoints in different point clouds are matched using the feature histograms obtained. Statistical methods are used to compare generated histograms. Thus, the two closest similar points between the different point clouds are matched. It is expected that the models with the most corresponding points belong to the same object. Euclidean distance between corresponding keypoints in the two point cloud is calculated. It has been accepted that the points are shorter than 10 mm. The positional accuracy of the matched points has been examined. Iterative Closest Point (ICP) was applied to the matching point clouds for this purpose. As a result, the graphics were generated that showed correct matching ratio and root mean square error. As a result, there are different approaches about 3D object recognition in literature. This study aimed to compare different keypoint detector and descriptor algorithms. Intrinsic Shape Signature (ISS) is keypoint detector algorithms. Point Feature Histograms (PFH) and Fast Point Feature Histograms (FPFH) are keypoint descriptor algorithms. The results of this study will provide guidance for future studies.
Authors and Affiliations
Muhammed Enes Atik, Abdullah Harun İncekara, Batuhan Sarıtürk, Zaide Duran, Dursun Zafer SEKER
Keywords
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- EP ID EP597244
- DOI 10.30897/ijegeo.551747
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