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Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud

Kun Fang1, Kaiming Xu2, Zhigang Wu3

  • 1Information and Big Data Management Center, Southwest University of Finance and Economics, No. 555, Liutai Avenue, Chendu 611130, China.

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|January 11, 2024
PubMed
Summary

This study introduces a novel 3D point cloud segmentation algorithm using depth cameras for unsupervised class segmentation of large-scale models. The method achieves high accuracy and speed, outperforming existing approaches with a 90.2% Intersection over Union (IoU).

Keywords:
clusteringdepth camerapoint cloud segmentationunsupervised classificationvoxelization

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Area of Science:

  • Computer Vision
  • 3D Data Processing
  • Machine Learning

Background:

  • Accurate segmentation of large-scale 3D point clouds is crucial for applications like robotics and augmented reality.
  • Existing unsupervised methods often struggle with scalability and maintaining high accuracy.

Purpose of the Study:

  • To develop an efficient and accurate unsupervised 3D point cloud segmentation algorithm for large-scale models.
  • To leverage depth camera data for improved segmentation performance.

Main Methods:

  • Utilized depth information from a depth camera.
  • Applied voxelization to reduce point cloud size.
  • Employed density and distance-based clustering for voxel segmentation.

Main Results:

  • Achieved high segmentation accuracy and fast processing speeds on diverse large-scale point clouds.
  • Demonstrated superior performance compared to recent similar works.
  • Attained an average Intersection over Union (IoU) of 90.2% on a custom benchmark dataset.

Conclusions:

  • The proposed algorithm offers an effective solution for unsupervised class segmentation of large-scale 3D point clouds.
  • The integration of depth data and voxelization enhances segmentation efficiency and accuracy.