Classify point clouds using PointNet(点群深層学習による点​群の分類)

Version 1.0.1 (23.1 MB) by Kenta
This demo shows how to classify point clouds using a method using deep learning for PointNet.


Updated 27 Nov 2020

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This example shows how to train a PointNet [1] network for point cloud classification. Point cloud data is acquired by a variety of sensors, such as lidar, radar, depth cameras, and iPad LiDAR. This example trains a PointNet classifier on 3D point clouds scanned by iPad LiDAR. Since this example just aims to show how to implement PointNet claasifier with MatLab, identical point clouds were trained and tested to classify. Please use your data to explore more. Note that this example is created based on the Matlab official document [2].
この例では、3次元点群を深層学習点群学習の手法(PointNet)によって、分類します。PointNet [1]では、点群を入力として、それのカテゴリーを返します。この例はMATLABの公式ドキュメント [2]を参考に作成しています。iPad LiDARにより取得した点群をサンプルデータとして用います。訓練データやテストデータとなる点群はデータストアと呼ばれるものに格納され、メモリを大きく消費することなく、効率よく学習や検証を行うことができます。ここでは点群用の自作のデータストアを利用します。

[1] Charles, R. Qi, Hao Su, Mo Kaichun, and Leonidas J. Guibas. “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 77–85. Honolulu, HI: IEEE, 2017.
[2] Point Cloud Classification Using PointNet Deep Learning
[3] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.” In 2015 IEEE International Conference on Computer Vision (ICCV), 1026–34. Santiago, Chile: IEEE, 2015.

Cite As

Kenta (2023). Classify point clouds using PointNet(点群深層学習による点群の分類) (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
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