How to Perform Manual Classification for Deep Learning Using CloudCompare

In this slide, how to perform manual classification for deep learning was introduced. The software used for manual classificaiton was CloudCompare.
This presentation was part of a workshop for CloudCompare users held in Tokyo on April 17, 2024.

Transcript

1. How to Perform Manual Classification for Deep Learning Using CloudCompare ImVisionLabs Inc., CEO Kenta Itakura, Ph.D
2. Trend of new functions in CloudCompare 2 iPhone12 LiDAR  Interview of Dr. Daniel by Eugene in YouTube  One trend of new functions in CloudCompare is Manual Segmentation Interview with Daniel Girardeau-Montaut, Click 3D Episode 50 https://www.youtube.com/watch?v=sBpi1yHC4xA
3. Manual Classification tool in CloudCompare 3 iPhone12 LiDAR  Introduction to the cloud layers function for manual classification
4. Need for manual labeling 4 iPhone12 LiDAR  The figure below shows the automatic classification using deep learning Point cloud Automatic classification result  Annotation data is required for training classification models  Manual classification is needed for training data
5. Example of point cloud processing: Tree segmentation 5
6. 上空から取得した樹木の解析について 6  より広範囲のデータの解析が可能  UAVレーザー測量により森林を計測し、樹木解析を実行 ※ Data from the Tokyo Metropolitan Government's Digital Twin Realization Project is used
7. Need for manual labeling 7 iPhone12 LiDAR  Manual classification is also required for evaluating segmentation results • Labeling is also needed to verify the accuracy of studies that count the number of trees Itakura, K., et al. (2021). Estimating tree structural parameters via automatic tree segmentation from LiDAR point cloud data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 555-564.
8. Point cloud segmentation: Our work with Tepco 8  Use case of 3D point cloud data to improve the efficiency of patrols of power distribution facilities  Classification into Vegetation, Wire and Ground ◼ Ground ◼ Vegetation ◼ Wire Point cloud colored by elevation Auto Classification result
9. Point cloud segmentation: Our work with Mr. Takata 9  Automatically detect “traces of a path” from point clouds  The “traces of a path” leads to places where stones were once extracted for historical structures 高さごとに色分けした図 自動分類した結果 Detected potential traces of a path Input point cloud
10. How to use CloudLayers: 10 iPhone12 LiDAR  Plugins -> Cloud layers iPhone12 LiDAR
11. How to use CloudLayers: 11 iPhone12 LiDAR  The panel is opened
12. How to use CloudLayers: 12 iPhone12 LiDAR  Select your target class
13. How to use CloudLayers: 13 iPhone12 LiDAR  button : Start Manual classification
14. How to use CloudLayers: 14 iPhone12 LiDAR  Circle radius can be changed  Use a smaller radius for painting small areas and a larger radius for larger areas
15. How to use CloudLayers: 15 iPhone12 LiDAR  Ground extraction made manual classification easy
16. How to use CloudLayers: 16 iPhone12 LiDAR  Ground extraction made manual classification easy
17. How to use CloudLayers: 17 iPhone12 LiDAR  Ground extraction made manual classification easy
18. Example of manual classification 18 iPhone12 LiDAR  Open data by Tokyo was manually labeled  Cloud layers function enabled efficient manual classification
19. Summary 19 iPhone12 LiDAR  Introduced Cloud layers function for manual classification  Segment and Merge functions are available for more detailed labeling  Data from the Tokyo Metropolitan Government's Digital Twin Realization Project is used
20. Supplementary material: How to download point clouds 20 iPhone12 LiDAR