The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 221–227, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-221-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 221–227, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-221-2022
 
30 May 2022
30 May 2022

ROBUST INDOOR POINT CLOUD CLASSIFICATION BY FUSING LSTM NEURAL NETWORKS WITH SUPERVOXEL CLUSTERING

M. J. Li1, L. H. Wang1, Z. H. Cai1, M. S. Yang1, R. J. Wu1, and M. M. Yao2 M. J. Li et al.
  • 1Guangdong Power Grid Corporation,Guangzhou, China
  • 2Shenzhen University, Shenzhen, China

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.