feat(viewer): Implement data-driven coloring for 3D point cloud#26
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This commit enhances the 3D viewer to provide a more meaningful visualization of the underlying data. Previously, points in the cloud were colored with a simple HSL gradient based on their index. This change implements data-driven coloring, where the color of each point is derived from its corresponding 3D vector in the .k3d metadata file. The implementation normalizes the position of each point within the overall bounding box of the dataset and maps the resulting [0, 1] values to the R, G, and B color channels. This ensures that points that are close to each other in the embedding space (after PCA) are rendered with similar colors, providing an intuitive visual representation of the data's structure. This change completes Phase 3 of the project roadmap and provides a foundational visualization for future work on navigation and interaction within the 3D knowledge space.
danielcamposramos
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Sep 7, 2025
…oring feat(viewer): Implement data-driven coloring for 3D point cloud
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This change implements data-driven coloring for the 3D point cloud viewer. The color of each point is now determined by its position in the 3D space, providing a more meaningful visualization of the data.