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

arXiv: https://arxiv.org/abs/2403.05950 · HTML: https://mlsanity.com/paper/2403.05950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions](https://mlsanity.com/paper/1905.06292/) (2019)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)
- [PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions](https://mlsanity.com/paper/2003.08624/) (2020)
- [Visual Analysis of Discrimination in Machine Learning](https://mlsanity.com/paper/2007.15182/) (2020)
- [AGO-Net: Association-Guided 3D Point Cloud Object Detection Network](https://mlsanity.com/paper/2208.11658/) (2022)
- [Remote Assistance with Mixed Reality for Procedural Tasks](https://mlsanity.com/paper/2208.03261/) (2022)
- [Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks](https://mlsanity.com/paper/1805.00322/) (2018)
- [TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network](https://mlsanity.com/paper/2110.03170/) (2021)

## Built on by (most cited first, 1 of 1)

- [Overview and Comparison of AVS Point Cloud Compression Standard](https://mlsanity.com/paper/2602.08613/) (2026)

## Abstract (arXiv)

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