Structured point clouds organize 3D data points in a 2D indexed grid, based on the scanner’s rotational position and angular increments. This structured format facilitates a systematic approach for identifying each point’s position relative to its neighbours and the scanner’s centre, allowing for efficient data organization and precise spatial referencing within each Setup.
Advantages
Efficient Processing: The ordered arrangement simplifies processing tasks, such as computing normals, generating meshes, and creating textures.
Enhanced Querying: Structured data allows for efficient spatial querying, as points maintain predictable neighbour relationships.
Editable Links: The relative positions of Setups in structured point clouds can be modified and adjusted over time, enhancing flexibility in editing the registration.
Disadvantages
Complex Data Architecture: Structured point clouds require storing the position and orientation of each Setup, with points in each Setup referenced accordingly. This adds an additional layer of organization within the project, as each Setup must maintain its spatial relationship independently.
Increased Storage Needs: Maintaining the structure across multiple Setups often leads to duplicated points and overlaps, increasing project size and storage requirements.