WolkenVision offers an AI‑driven spatial intelligence platform that automates the processing of massive 3D point‑cloud datasets, delivering real‑time preview, quality inspection, semantic segmentation, and rapid generation of BIM‑ready geometry. By integrating multi‑source LiDAR and photogrammetry data with deep‑learning models, it fills gaps, denoises, and converts point clouds into engineering‑grade digital twins, enabling AEC and infrastructure teams to streamline workflows and reduce manual effort.
Funding
Funding not disclosed
Founders
Product
Problem
Infrastructure and construction projects generate massive 3D point cloud datasets that are difficult to visualize, clean, segment, and convert into usable BIM models. Existing workflows require manual inspection, extensive processing time, and specialized expertise, limiting the speed and accuracy of digital twin creation and asset management.
Solution
WolkenVision provides an AI‑driven spatial intelligence platform that automates the end‑to‑end processing of large point clouds. Its suite—WolkenInsight, WolkenCept, WolkenRefinery, and WolkenShape—covers high‑speed preview, quality inspection, semantic segmentation, gap completion, correction of misclassifications, and rapid generation of BIM‑ready geometry. The system ingests multi‑source data (LiDAR, photogrammetry), runs deep‑learning models to identify and fill missing areas, and outputs standardized formats with coordinate conversion. Deployable via Docker containers and open APIs, the platform integrates directly into existing digital workflows, enabling engineers to create accurate, engineering‑grade digital twins with minimal manual effort.
Target Audience
Primary customers are engineering firms, construction contractors, and asset‑management teams in the AEC, infrastructure, and smart‑city sectors that require fast, accurate conversion of point‑cloud data into BIM and digital‑twin models.
Features
- Real‑time preview and navigation of billion‑point clouds with automatic format conversion
- AI‑based quality analysis that detects occlusions, denoises data, and fills missing regions
- High‑precision semantic segmentation across dozens of infrastructure classes using a fusion deep‑learning model
- Confidence‑driven correction tool (WolkenRefinery) for batch fixing of misclassified points
- Automated generation of BIM‑compatible geometry (planes, beams, columns) and “white” building models for rapid reconstruction
- Containerized deployment and open REST API for seamless integration with CAD/BIM software and enterprise pipelines
- Multi‑format export with coordinate system transformation and support for major scanning devices