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1707250PublishedVol 8 · Issue 8

Efficient Sampling Techniques for Point Clouds

Hyacinthe Hamon

Subject area: Science,Engineering and Technology  ·  Area of research: Computer Vision and 3D Data Processing

Abstract

This paper introduces BOLT (Bilateral filtering and Octree Lightweight Technique), a novel, fast, and parameter-free method for up-sampling point clouds. We leverage the structural efficiency of the octree data structure and detail-preserving properties of the bilateral filter to achieve a fast and parameter-free sampling method. Unlike the current state-of-the-art techniques, BOLT does not require any parameters, deep learning, fine-tuning, or any type of training, making it a suitable candidate for real-time applications. BOLT understands the underlying structure of the point cloud by dividing the point cloud into a hierarchical octree structure. Empty children are filled in the octree, and outliers are smoothed using a bilateral filter.

How to cite this paper

Hyacinthe Hamon "Efficient Sampling Techniques for Point Clouds" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 684-690
Hyacinthe Hamon "Efficient Sampling Techniques for Point Clouds" Iconic Research And Engineering Journals, vol. 8, no. 8, Feb. 2025
Hyacinthe Hamon (2025). Efficient Sampling Techniques for Point Clouds. Iconic Research And Engineering Journals, 8(8).
Hyacinthe Hamon "Efficient Sampling Techniques for Point Clouds" Iconic Research And Engineering Journals, vol. 8, no. 8, Feb. 2025.
@article{1707250,
      author = {Hyacinthe Hamon},
      title = {Efficient Sampling Techniques for Point Clouds},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {8},
      pages = {684-690},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707250.pdf},
      abstract = {This paper introduces BOLT (Bilateral filtering and Octree Lightweight Technique), a novel, fast, and parameter-free method for up-sampling point clouds. We leverage the structural efficiency of the octree data structure and detail-preserving properties of the bilateral filter to achieve a fast and parameter-free sampling method. Unlike the current state-of-the-art techniques, BOLT does not require any parameters, deep learning, fine-tuning, or any type of training, making it a suitable candidate for real-time applications. BOLT understands the underlying structure of the point cloud by dividing the point cloud into a hierarchical octree structure. Empty children are filled in the octree, and outliers are smoothed using a bilateral filter.},
      month = {February},
  }