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How to Perform Manual Classification for Deep Learning Using CloudCompare

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.

Kenta Itakura

April 13, 2024

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  1. 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
  2. Manual Classification tool in CloudCompare 3 iPhone12 LiDAR  Introduction

    to the cloud layers function for manual classification
  3. 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
  4. 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.
  5. 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
  6. 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
  7. 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
  8. Example of manual classification 18 iPhone12 LiDAR  Open data

    by Tokyo was manually labeled  Cloud layers function enabled efficient manual classification
  9. 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