Classification of Point Clouds for Indoor Components Using Few Labeled Samples

Remote Sensing

Hangbin Wu¹  Doudou Zeng¹  Huimin Yang¹  Chun Liu¹  Shengyu Huang²  Chi Guo³  Long Chen⁴  Hao Zhang⁵ 

[1] College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China;Institute of Geodesy and Photogrammetry, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland;National satellite positioning system engineering technology research center, Wuhan University, Wuhan 430072, China;School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China;

关键词: few labeled samples;    point clouds;    classification;    indoor scenario;   

DOI  :  10.3390/rs12142181

来源: DOAJ

【 摘 要 】

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89%–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

【 授权许可】

Unknown