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The pioneer in mobile-first 3D photogrammetry and spatial environment capture.
Turn standard photographs and laser scans into high-precision 3D reality meshes for infrastructure and smart city development.
ContextCapture, a flagship solution by Bentley Systems (transitioning into the iTwin Capture suite for 2026), is an enterprise-grade photogrammetry and reality modeling engine designed to produce highly detailed 3D meshes from simple photographs and point clouds. Its technical architecture is built on advanced computer vision algorithms that perform rigorous aerotriangulation and hybrid reconstruction. By 2026, ContextCapture has solidified its market position as the backbone for heavy infrastructure digital twins, leveraging GPU acceleration and distributed computing (ContextCapture Center) to process massive datasets ranging from individual bridge components to entire city blocks. The software excels in 'hybrid' processing, seamlessly merging RGB imagery with Lidar point clouds to achieve millimeter-level accuracy. As Bentley moves toward a cloud-first ecosystem, ContextCapture provides the foundational geometric data for iTwin applications, enabling real-time asset monitoring and predictive maintenance. It remains a critical tool for surveyors, civil engineers, and urban planners who require scalable, geometrically accurate models that integrate directly into CAD and GIS workflows without the high overhead of manual 3D modeling.
Combines RGB imagery with Lidar point clouds using a weighted algorithm to fill gaps in areas with poor texture.
The pioneer in mobile-first 3D photogrammetry and spatial environment capture.
Convert real-world images into photorealistic 3D materials and HDR environments with AI-driven precision.
High-fidelity neural surface reconstruction for turning 2D video into detailed 3D digital twins.
Transform disorganized, unconstrained photo collections into high-fidelity 3D neural reconstructions.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Proprietary level-of-detail (LOD) format that allows massive meshes to be streamed over the web.
Allows multiple 'Engines' across a network to process a single project in parallel.
AI-driven point cloud classification to separate terrain from vegetation and structures.
Drapes thermal infrared imagery over 3D geometry for heat loss or energy audits.
Mathematical optimization of camera parameters including lens distortion and interior orientation.
C++ and Python interfaces for automating reconstruction workflows and UI customization.
Creating an accurate 3D model of an entire city for flood simulation.
Registry Updated:2/7/2026
Inspecting hard-to-reach structural components without scaffolding.
Measuring inventory volume in a mining site with high accuracy.