Industry Pain Points

2D Design
Limits

Flat drawings obscure spatial relationships, increasing errors.

Aging Data
Gaps

Renovations rely on rough measurements, risking structural misjudgments.

Hidden
Hazards

Walls and internal defects require destructive testing, raising costs.

Data
Silos

Isolated project phases slow decisions with document-driven workflows.

Key Technologies

Segmentation

Point Cloud Processing

Semantic Modeling

Using deep neural networks to perform high-precision semantic segmentation of residential building point clouds and images, automatically identifying key components such as walls, doors and windows, stairs, and roofs.


 Combined with rule-based post-processing to optimize the results, this approach provides comprehensive and accurate semantic data support for geometric reconstruction and functional analysis.

Deep neural networks enable high-precision semantic segmentation of residential point clouds and images,
automatically identifying walls, doors, windows, stairs, and roofs with rule-based post-processing
to deliver comprehensive, accurate semantic data for geometric reconstruction and functional analysis.

Neural networks classify room functions while detecting performance risks (lighting/CFD/energy), producing BIM-integrated reports for data-driven facility management.

Product Process

Deeply aligned with user needs, building an optimized experience closed-loop operation

Business Values

Innovative spatial data production methods balancing precision, cost and experience

Precision Modeling

Cloud-based semantic segmentation and AI reconstruction
Delivering 1:1 digital twins for reliable design/construction data

Risk Control

Proactive structural risk detection
Shifting accident prevention from reactive to predictive

Green Enablement

Optimizing material use and digital archives
Supporting sustainable retrofits

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