ROBUST INDOOR POINT CLOUD CLASSIFICATION BY FUSING LSTM NEURAL NETWORKS WITH SUPERVOXEL CLUSTERING
Keywords: Indoor Classification, LSTM, Supervoxel, Point Cloud, Machine Learning
Abstract. To address the problems of lack of training data and inaccurate classification of existing 3D point cloud data segmentation and classification methods, this paper proposes a high-precision classification algorithm for indoor point clouds by fusing LSTM neural network and super voxels. The algorithm first performs super voxel segmentation on the original point cloud and uses it as the basic unit for machine learning classification, and then introduces LSTM (Long Short-Term Memory) neural network to model the super voxel domain relationship and optimize the classification results. Finally, the accuracy of the proposed method is evaluated based on open dataset, and the experimental results show that 83.2% classification accuracy can be achieved in the open dataset.