Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach

arXiv:2403.05950v1 Announce Type: new
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; …

abstract applications arxiv autonomous become classification cs.ai cs.cv deep learning gru hot hybrid hybrid approach lstm navigation network neural network objects paper reality recurrent neural network research spot strategy type virtual virtual reality