Automates denoising, stitching, registration, and layering to deliver clean, high-precision point clouds, eliminating manual work for accurate BIM foundation.

Recognizes walls, columns, doors, windows, and equipment via deep learning to create attributed BIM models, replacing traditional manual modeling.

Automatically extracts wall cracks, roof leaks and more from point clouds and textures, outputting quantifiable reports for maintenance optimization.

Automates denoising, stitching, and registration to generate clean, high-fidelity 3D data, avoiding tedious manual processing.

AI-powered identification of beams, columns, and more from point clouds to rapidly create attributable 3D models with minimal manual input.

Detects subtle flaws using high-density point clouds and textures, delivering inspection reports for precise condition assessment.

Spatially segments high-density point clouds using smart algorithms to rapidly distinguish equipment, pipelines, and structural zones, enabling targeted analysis.

Building BIM by automatically extracts equipment types, pipe routing, and interface parameters via AI recognition and geometric analysis, enabling defect detection.

Reconstructs point clouds into attributed BIM models through automated geometric fitting and semantic reasoning, drastically reducing manual modeling effort.

Algorithmically segments point clouds to distinguish beams, slabs, and more, eliminating point-by-point manual processing and accelerating data readiness.

Automates extraction of wall thickness, beam sections, and pipe routing. Intelligently analyzes deviations and clash risks with report generation for quality control.

Generates attributed BIM from site scans via geometric fitting and semantic recognition, enabling clash detection, digital inspection, and progress validation.

Efficiently segments complex building point clouds using spatial geometry and AI to differentiate walls, structural elements, and MEP systems.

Automatically identifies wall cracks, equipment positions, pipe routing, and deformations, outputting smart reports for precise maintenance planning.

Converts segmented point clouds into engineering-ready BIM models via semantic algorithms, supporting digital maintenance and long-term asset management.

Automatically generates BIM models for pipeline engineering from point cloud data.

Extracts potential hazards including encroaching structures, ground subsidence, and third-party intrusions.

Automatically identifies pipelines, supports, valves, and chambers in complex environments.

Quickly segment areas such as buildings, roads, pipelines, and other elements into manageable units, supporting refined management and detailed planning of multiple sectors.

Automatically extract building height, pipeline data, and other parameters, analyze congestion and hidden risks, supporting smart planning and risk forecasting.

Quickly convert point cloud data into usable BIM models for real city environments, helping with immersive smart city management and digital asset management.

Efficiently segment point cloud data, generating clear, manageable units for future optimization and applications in various scenarios.

Automatically extracts spatial attributes and relationships from point cloud data, enabling interaction, risk analysis, and virtual footpath simulation to improve planning and decision-making for both real and simulated environments.

Rapidly converts point cloud data into BIM models with relevant attributes, supporting immersive visualizations, digital asset management, and real-world environment simulations for better decision-making and resource management.

Efficiently segment point cloud data into clear, manageable units, supporting subsequent optimization and applications across multiple scenarios.

Automatically extract spatial attributes and relationships, supporting interaction, risk analysis, and virtual footpath simulation.

Quickly generate lightweight BIM models, with real environment details, to support immersive display and digital asset management.

Quickly break down operational areas, obstacles, and environments into manageable spatial units to help in route planning and operations.

Automatically acquire environmental layouts, equipment distribution, and spatial relationships, supporting obstacle avoidance solutions and intelligent route optimization.

Convert current environment data into interactive 3D models, helping robots adapt quickly for navigation and post-operation maintenance.