☁️

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.

$0 (bootstrapped) raised 🚀 Early Stage

Founded 2020 • Not publicly specified; case study with Japanese partner (SmartScape/ScanX) mentioned

Website → ← All Tools

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.

How You Use It

🚀

Delivery Method

SaaS API

🔌

Integrations

Autodesk Revit AutoCAD IFC (Industry Foundation Classes)

Disciplines

Architecture, Structural Engineering, MEP Engineering, Construction, Facility Management

Project Phases

Construction Documentation, Construction Administration, Operations/FM

Project Types

Commercial, Industrial, Healthcare, Infrastructure, Institutional

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

• 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

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

Website →

📚 Research Sources & Data Quality Last verified: 2026-02-01

Data Sources (6)

✅

Verified Data (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

Our Commitment: We only include verified data from official sources. If information isn't publicly available, we mark it as "Not publicly specified" rather than guessing.