Digital Twins: the missing pieces we can solve with Machine Learning (quantblog.wordpress.com)

Digital Twins promise rich, usable proxies of real-world sites but are held back by expensive, data-heavy capture and laborious modelling workflows. Current pipelines rely on LIDAR (30k devices, ~5k/yr software) producing 20–200GB pointclouds or photogrammetry that needs 400+ photos and yields dense textured meshes. The missing piece is automated conversion of those raw scans into “lite” 3D CAD models (e.g., walls as quad planes, pipes as cylinders) with semantic tags (material, function, part IDs, geolocation, or BIM .ifc classes) so models are compact, searchable and usable by humans, programs and LLM/Generative AI. Machine Learning can close that gap: models that turn pointclouds, photogrammetric meshes or even 360° panoramas into CAD geometry and automatically tag components will radically cut scan-to-CAD cost and unlock mass adoption. The author reports prototype results (detecting walls/edges and pipe centerlines from pointclouds and panoramas) and estimates MVP development by small teams in ~6 months at roughly $600k. Given a ~$20B Digital Twin market (40% CAGR) and ~$5B/year currently spent on manual scan-to-CAD, such ML-driven tooling would enable ubiquitous lightweight 3D maps, real-time construction and industrial asset models, VR/AR workflows, and robot training — making investment in this engineering tractable and high-impact.