Source review
Confirm scope, accuracy, formats, coordinates and intended use.
Capabilities
Production organized around a usable result
Source data, accuracy and intended use define the workflow. These capabilities can be combined without forcing the project into a fixed package.
Scan quality review
Check coordinate systems, registration, point density, missing areas, reflective noise and data partitions.
Building and MEP reconstruction
Rebuild walls, slabs, openings, structure, piping and major equipment from supported scan evidence.
Accuracy planning
Define measurable tolerances for overall form, critical dimensions and component location before batch work.
Scan to BIM delivery
Organize outputs by floor, discipline or system and include review views and an unresolved-issues list.
Delivery Standard
Acceptance criteria before delivery
Specific values depend on the source data and target environment. Applicable criteria are agreed at the start instead of relying on vague quality labels.
| Typical input | LAS, LAZ, E57, PLY and PTS |
|---|---|
| Primary output | BIM, mesh models and engine assets |
| Quality focus | As-built reconstruction and structure |
| Asset hierarchy | Objects organized by building, floor, system, equipment or component |
| Runtime fit | Optimization for the target browser, desktop or mobile environment |
| Review method | Pilot approval, milestone previews, issue list and final validation |
Workflow
Approve a pilot before batch production
A representative pilot validates source quality, accuracy, visual language, hierarchy and performance early in the project.
Pilot approval
Lock hierarchy, detail, materials and performance on a representative asset.
Batch production
Model, review and preview assets against the agreed manifest.
Final validation
Verify files, versions, hierarchy, performance and delivery notes.
FAQ
Frequently asked questions
What accuracy can point cloud to BIM achieve?
It depends on capture equipment, station layout, registration, density and occlusion. A representative area should be tested before setting tolerances.
Which point-cloud formats are supported?
LAS, LAZ, E57, PTS and PLY can be assessed. Large datasets should include coordinate and capture notes.
How are missing scan areas handled?
Drawings, photos or field dimensions can supplement them. Unsupported geometry is identified as inferred rather than treated as scan evidence.
Does a point-cloud model need optimization?
Usually yes for browsers and digital twins. Data should be partitioned around space, view distance and object-query requirements.