Paper Info
Title
A Training Free Technique For 3d Object Recognition Using The Concept Of Vibration, Energy And Frequency *
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 relation-ship 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 pro-posed 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 ob-jects) 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 exper-imental results demonstrate that the proposed technique has outperformed other state-of-the-art tech-niques 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.(c) 2021 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.cag.2021.01.014
COMPUTERS & GRAPHICS-UK
Keywords
DocType
Volume
3D object recognition, 3D feature descriptors, Texture-less object detection
Journal
95
ISSN
Citations 
PageRank 
0097-8493
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Piyush Joshi100.34
Alireza Rastegarpanah274.66
Rustam Stolkin3346.91