A High-Precision Indoor Point Cloud Classification Method Jointly Optimized by Super Voxel Random Forest and LSTM Neural Network

  Objectives  To address the problem of internal inconsistency of classification targets in existing three dimensional(3D) point cloud data segmentation and classification methods. we propose a high-precision classification method for indoor point cloud jointly optimized by super voxel random forest and long short-term memory (LSTM) neural network.

  Methods  The method takes into account that the super voxel structure has the characteristics of internal feature consistency, divides the original point cloud into super voxels, and uses super voxels as the basic unit for multivariate feature calculation to build a super voxel random forest classification model for indoor point cloud to achieve coarse classification of point cloud data. On this basis, LSTM is introduced to train and predict the neural network model for the hyper voxel neighborhood connectivity of coarse classification to achieve the optimization of hyper voxel coarse classification results. The validity and accuracy of the proposed classification method are verified based on the open dataset.

  Results  The results show that the classification accuracy of the proposed classification method can reach 83.2% for 13 types of elements in the open dataset. The training data of the LSTM optimization network proposed in this paper used only the label information of region 1 for model training, while other deep learning frameworks used regions 1-5 for model training, so from the perspective of training data requirements, the point cloud data classification framework proposed in this paper can achieve a relatively better prediction result with a small portion of the training data set. The super voxel-based LSTM optimization method approach has high classification accuracy on objects with obvious set features such as ceiling, floor and wall, however, it is inferior to the deep learning algorithm RandLA-Net in classifying objects with complex structures such as chair, sofa and bookcase.

  Conclusions  In this paper, we consider the association characteristics between different types of elements embedded in the connection relations among super voxels, and introduce LSTM to train and predict the model for the coarse classification of super voxel neighborhood connection relations to achieve the optimization of coarse classification results of super voxels. The proposed method can achieve better classification accuracy when trained with small samples compared with the classical deep learning framework.