AI for Laser Scanning
AI for analyzing laser scan and point cloud data.
Definition
AI for laser scanning helps analyze point cloud data from laser scans. AI can identify objects, measure deviations, and create models from scan data. This improves the value of reality capture investments.
In Depth
Laser scanning (LiDAR) produces point clouds with millions of data points — a rich 3D representation of existing conditions that contains enormous information but is difficult to use directly. AI transforms raw point cloud data into structured, usable building information.
Object recognition is the first step. AI identifies building elements within the point cloud: walls, floors, ceilings, columns, beams, pipes, ducts, and equipment. Each recognized object is classified by type and measured for dimensions. This automated recognition replaces hours of manual tracing and interpretation by scan technicians.
Deviation analysis compares the as-built point cloud against the design model to identify construction deviations. AI highlights areas where the built conditions differ from the design — a wall that is 2 inches out of plumb, a slab that is half an inch low, a mechanical penetration that is 6 inches from its intended location. These deviations are documented with precise measurements and locations, giving the project team objective data for addressing field conditions.
Examples
Identifying objects in scans
Measuring deviations
Creating models from scans
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
AI for laser scanning helps analyze point cloud data from laser scans. AI can identify objects, measure deviations, and create models from scan data. This improves the value of reality capture investments.
Identifying objects in scans. Measuring deviations. Creating models from scans.
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