Problem: Large-scale 3D point cloud datasets, with hundreds of millions of points, are often very slow or fail to open in existing desktop viewers due to memory limitations.

  Solution:
   * I designed a hybrid architecture: a Rust native module handles the
     heavy data processing, while WebGPU manages the rendering.
   * Instead of loading the entire dataset into memory, I implemented a
     custom Level of Detail (LOD) system that dynamically loads only the
     necessary tiles.
   * Thanks to this architecture, it's theoretically possible to visualize
      a limitless number of points, provided you have the storage space.

  Result:
  The result is 'PointPeek', a desktop app that smoothly explores a 500M
  point (10GB) dataset at 60fps.

  Check it out in the demo video below:
   * Demo Video: https://x.com/leesyub/status/1963623557117731144

  Current Challenge:
  I'm currently tackling the challenge of rendering the entire scanned
  point dataset of the city of Vancouver, Canada. (Though, just
  downloading the data seems like it will take a few days.)

  Ultimate Goal:
  After that, the ultimate goal is to integrate a local AI model via
  Ollama to implement features for querying and analyzing the data using
   natural language.

  Technical questions or feedback are welcome.

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