Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model

Author:

Kowalczuk Zdzisław,Szymański Karol

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference23 articles.

1. Deep convolu-tional neural networks for sentiment analysis of short texts;dos Santos;Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers,2014

2. Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., and Posner, I. (2016). Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. CoRR, abs/1609.06666. URL http://arxiv.org/abs/1609.06666.

3. Deep residual learning for image recognition;He;Proceedings of the IEEE conference on computer vision and pattern recognition,2016

4. Hu, X. and Yuan, Y. (2016). Deep-learning-based classification for DTM extraction from ALS point cloud. Remote Sensing. doi:10.3390/rs8090730.

5. Jing Huang and Suya You (2016). Point cloud labeling using 3D Convolutional Neural Network. In 2016 23rd International Conference on Pattern Recognition (ICPR). doi:10.1109/ICPR.2016.7900038.

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