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What does tcp PointCloud Editor offer for data from SLAM systems?
Tcp PointCloud Editor is the definitive solution for professionals seeking to maximize the value of their captures with mobile devices. The software combines precision, versatility, and power to transform complex point clouds into high-quality technical deliverables.
Its key capabilities include:
Productivity with Artificial Intelligence: Automatic classification using models trained for indoor and outdoor environments, optimizing office time by intelligently separating structural elements and furniture.
Expert Attribute Management: Advanced processing of intensity and color, allowing for a realistic and detailed visualization of the captured scene.
Immersive Visualization: Integration of 360º panoramic images synchronized with the 3D environment for precise vectorization and measurement leveraging point depth.
Total Interoperability: Real-time synchronization with CAD platforms and direct export to BIM (IFC) and GIS (GeoTIFF) formats.
How are routes and images from a SLAM system imported?
The software facilitates data incorporation through the Import SLAM command set, designed to manage both the traversed trajectory and the graphic material associated with the project.
This process allows linking capture stations with the point cloud, enabling advanced navigation tools that facilitate visual inspection of the surveyed environment.
What tools are available to visualize and navigate the trajectory of a mobile scan?
Project navigation is performed intuitively through the SLAM Route Viewer. This tool allows the professional to move through a 2D schematic of the route and select specific stations to view the associated 360-degree images.
This viewer is synchronized with the main 3D scene, allowing distance and area measurements, as well as vector drawings, directly on the photograph.
How can the quality of a SLAM point cloud be improved before generating models?
To ensure professional results in subsequent modeling, a cleaning and refinement workflow is recommended:
AI Classification: Use automatic models to filter noise and classify elements such as walls, floors, or vegetation in bulk.
Precision manual cleaning: Use tools such as Selection by Polygon to isolate and hide or delete reflections in mirrors or areas with redundant data captured during movement.
Filtering by Geometry: Apply selection commands by plane or neighborhood to refine specific surfaces and ensure that only valid points are part of the final digital model.
