A Blender addon for visualizing, editing, filtering, rendering, and converting point cloud PLY files within the 3D viewport.
Point Cloud Visualizer is a Blender addon that allows users to import, visualize, edit, and render point cloud data (PLY files) directly within Blender's 3D viewport. It solves the problem of working with raw 3D scan data by providing tools for filtering, converting to meshes, and integrating point clouds into Blender's pipeline.
3D artists, VFX professionals, and developers working with photogrammetry, LiDAR scans, or other point cloud data who need to process and visualize this data within Blender.
It offers a seamless, all-in-one solution for point cloud manipulation inside Blender, eliminating the need for external software and enabling direct integration with Blender's modeling, texturing, and rendering workflows.
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Offers an all-in-one toolkit from loading PLY files to rendering, with filtering, conversion, and generation tools that keep point cloud processing within Blender's environment, as detailed in the feature list.
Includes debug shaders for depth, normals, and position, plus clipping planes and illumination controls, enabling detailed inspection and manipulation directly in the 3D viewport.
Can convert point clouds to mesh instances, particle systems, or traditional meshes, facilitating integration into Blender's rendering pipeline, as shown in the Convert and Generate sections.
Provides an external Python API for programmatic point cloud drawing, allowing customization and automation beyond the GUI, exemplified in the External API code snippets.
The free GitHub version is unmaintained and limited to Blender 2.81, forcing users to purchase the addon for current Blender compatibility, creating a barrier for budget-conscious teams.
Editing points does not update normals, and new points have random attributes, as admitted in the Edit section, hindering accurate post-processing workflows.
Features like Poisson Disk Sampling are explicitly noted as 'very slow' in the README, making them impractical for large datasets or time-sensitive projects.