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ScanToBIM

ML-powered point cloud to BIM conversion

Machine learning platform that automates the conversion of laser scan point cloud data to building information models (BIM) in Revit, AutoCAD, or IFC formats.

Pricing
$99/month or $199/month subscription tiers
Alternatives
Integrated Projects (IPX), Revizto, OpalAi - ScanTo3D & Floorplan
Who it's for
Architecture, Structural Engineering, MEP Engineering
$0 (bootstrapped) raisedEarly Stage
Founded 2020 · Not publicly specified; case study with Japanese partner (SmartScape/ScanX) mentioned
Website → ← All Tools
WHY IT EXISTS

Why This Tool Exists

The Problem

Manual point cloud to BIM conversion requires specialists to spend weeks tracing and classifying point cloud data, with typical projects involving 600+ million data points. This process creates bottlenecks in renovation, facility management, and retrofit projects, delaying project delivery and increasing labor costs by 40-60%.

The Solution

ML engine trained on 800+ GB of point cloud data automatically segments point clouds with color-coded object identification and converts classified data to BIM models in Revit, AutoCAD, or IFC formats. Platform claims to reduce conversion time by up to 50% while maintaining consistency and accuracy.

USAGE

How You Use It

Delivery Method
SaaSAPI
Integrations
Autodesk RevitAutoCADIFC (Industry Foundation Classes)
Disciplines
Architecture, Structural Engineering, Mep Engineering, Construction, Facility Management
Workflows
Design & BIM, Operations & Facility Management
Project Phases
Construction Documentation, Construction Administration, Operations/FM
Project Types
Commercial, Industrial, Healthcare, Infrastructure, Institutional
TRANSPARENCY

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:3D point cloud data from laser scanning
Optional:Project metadata, Reference BIM models, Quality checklists
Formats:E57 (3D imaging standard), LAS (ASPRS laser scan format)
Output
What you get
Format:Native BIM models in multiple formats
Fields:Classified point clusters by object type, Structural elements (walls, floors, columns, beams), MEP systems (pipes, ducts, electrical conduits), Architectural features (windows, doors, stairs), Dimensions and coordinates, Material classifications
Algorithm
How it works
Model:Proprietary ML engine trained on 800+ GB of point cloud data
Accuracy:Claims up to 50% time reduction in BIM modeling; specific accuracy metrics and validation methodology not publicly disclosed
Privacy
How your data is protected
Retention:Project-based data retention policy not publicly specified; data handling duration unclear
Training:ML engine trained on historical point cloud data; user data training policy not publicly specified
Compliance:Privacy policy mentions Fair Information Practices compliance, No SOC 2 or ISO 27001 certification mentioned
API
Integration
Endpoint:Contact vendor for API access
Method:Not publicly documented
USE CASES

Use Cases

  • ·Renovation and retrofit projects: Convert as-built laser scans to BIM for planning upgrades
  • ·Facility management: Create accurate BIM models of existing buildings for maintenance tracking
  • ·Historic preservation: Document and model complex historical structures for conservation planning
  • ·MEP system documentation: Automated extraction and modeling of mechanical, electrical, and plumbing systems
  • ·Quality assurance: Compare as-built conditions against design models for deviation analysis
PRICING

Pricing

Free
Trial with first complimentary BIM model conversion
Pro
$99/month or $199/month subscription tiers (specific feature differences not detailed)
Enterprise
Custom pricing for large-scale projects
Sources & Research NotesResearch date: 2026-02-01
Data Sources (6)
Fields Checked (9)
problem, solution, deliveryMethod, disciplines, projectPhases, projectTypes, yearFounded, algorithm-overview, input-output-formats
Not Found (8)
pricing-details, current-customer-base, specific-api-documentation, accuracy-metrics-published, soc2-or-iso-compliance, data-retention-policy, rate-limits, detailed-ml-architecture
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.
ALTERNATIVES

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