  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
