Version 1
: Received: 13 March 2024 / Approved: 13 March 2024 / Online: 13 March 2024 (10:00:29 CET)
How to cite:
Mousa, R.; Khezli, M.; Hesaraki, S. Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach. Preprints2024, 2024030775. https://doi.org/10.20944/preprints202403.0775.v1
Mousa, R.; Khezli, M.; Hesaraki, S. Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach. Preprints 2024, 2024030775. https://doi.org/10.20944/preprints202403.0775.v1
Mousa, R.; Khezli, M.; Hesaraki, S. Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach. Preprints2024, 2024030775. https://doi.org/10.20944/preprints202403.0775.v1
APA Style
Mousa, R., Khezli, M., & Hesaraki, S. (2024). Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach. Preprints. https://doi.org/10.20944/preprints202403.0775.v1
Chicago/Turabian Style
Mousa, R., Mitra Khezli and Saba Hesaraki. 2024 "Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach" Preprints. https://doi.org/10.20944/preprints202403.0775.v1
Abstract
Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.
Keywords
Point Cloud Classification; Virtual Reality; Hybrid Model; GRULSTM; GRU; LSTM
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
