A training free technique for 3D object recognition using the concept of vibration, energy and frequency. (April 2021)

• Record Type: Journal Article
• Authors: Joshi, Piyush; Rastegarpanah, Alireza; Stolkin, Rustam
• Is Part Of: Computers & graphics. Volume 95(2021)
• Page Start: 92
• Page End: 105
• Publication Date: 2021-04
• Subjects: 3D object recognition -- 3D feature descriptors -- Texture-less object detection; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.01.014
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: We present a local surface feature based 3D object recognition technique that is free from any training and handles texture-less objects. The technique makes a strong relationship among the different regions of an object using togetherness concept of Frequency, Energy and Vibration of points in a point cloud. We propose to present a 3D dataset of 10 texture-less objects consists of industrial and household objects. Our experimental results demonstrate that the proposed technique has outperformed other state-of-the-art techniques on the proposed dataset. Graphical abstract: Abstract: This paper presents a local surface feature based 3D object recognition technique that is free from any training and handles texture-less objects. Our technique is proposed based on building a strong relationship among the different regions of an object using the combination of Vibration, Energy and Frequency of points in a point cloud. The robustness of the proposed technique has been validated by comparing with top-rated training free recognition techniques on the Bologna dataset. Results show that the proposed technique has performed well and efficiently as top-rated techniques on this dataset. In real time scenario, captured scenes by an RGBD camera are cluttered with many unwanted objects and background. Most of the state-of-the-art techniques (techniques that are training free and recognize texture-less objects) have not experimented on such scenes in the literature. To observe the performance, we propose to present a 3D dataset of 10 texture-less objects (including industrial and household objects). Our experimental results demonstrate that the proposed technique has outperformed other state-of-the-art techniques on the proposed dataset. We also experiment on three very cluttered and occluded RGBD datasets (Challenge, Clutter and Willow). The poor performance of all techniques on these datasets has revealed the need for more robust techniques in the future.