ScanTo3D | OpalAi vs Kaedim

ScanTo3D by OpalAi is a 3D generation app for iPhone Pro and iPad Pro that turns LiDAR walkthrough scans into floor plans, point clouds, and BIM-ready models. You walk through a space with your phone, capture geometry in InstaCAD or LiDAR mode, and export digital twins in formats like OBJ, GLTF, STL, and USDZ. It targets property owners, contractors, and facility teams who need as-built documentation without hauling a dedicated scanner.

Compared to renting tripod LiDAR hardware, ScanTo3D trades equipment cost for device requirements and optional cloud processing. InstaCAD mode builds 2D floor plans and 3D CAD models in real time for spaces under 2,000 square feet, while LiDAR mode handles larger environments with no hard size cap though OpalAi recommends keeping each scan under about 5,000 square feet.

Site managers, real estate teams, and construction sales staff use it for quick floor plans, ANSI-compliant gross living area calculations, and textured 3D exports. OpalAi claims less than 1 inch of error per 30 feet and about 98% accuracy on generated floor plans and BIM objects.

Kaedim handles 3D generation for studios and brands that need production-ready assets from sketches, reference packs, product photos, and creative briefs. Upload your inputs, review staged models in a shared workspace, mark up changes, and approve assets before they ship to your engine or CAD handoff.

Instant mesh generators spit out geometry you still have to fix. Kaedim pairs proprietary 3D automation with expert artist assurance, style-guide alignment, and a review loop (look, mark up, compare, revise, approve) built for milestone-driven teams. Myth Studio reported about 4 hours per asset versus a 40-hour market average in a published case study.

Game studios use Kaedim for props, environments, and characters that match art bibles. Product teams resolve proportion and volume before CAD commits. E-commerce and marketing groups turn concepts into reusable product CGI without a physical shoot. ISO27001 certification, per-project data isolation, and a policy that your IP is not used to train shared models target confidential pre-release work.