Bridge the gap between captured reality and intelligent models. Workflows that transform scans into BIM-ready geometry and living digital twins of buildings and infrastructure.
The complete guide to predictive digital twins built from survey data: asset cells, a condition index you can defend, a graph, a declared decay mechanism, a graph attention forecaster in plain PyTorch, and a work order that stops at a budget line.
Predictive Digital Twins and Spatiotemporal Simulation Guide Read More »
From the NXT BLD 2026 stage and the reality capture panel: the local-first cognition stack for 3D spatial AI for AEC. Smart point cloud in, IFC and decisions out, in three days, not six weeks.
3D Spatial AI for AEC: The Offline Stack That Replaces 6 Weeks of BIM Work Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 4 October 2023 A 10-step Python Guide to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Space Occupancy 3D Modeling of Indoor Point Cloud Datasets. If you have experience with point clouds or data analysis, […]
3D Shape Detection for Indoor Modelling Read More »
