ScanTo3D | OpalAi vs DreamFusion

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.

DreamFusion generates 3D objects from text captions using a pretrained 2D text-to-image diffusion model instead of 3D training data. It optimizes a Neural Radiance Field (NeRF) so random-angle 2D renderings match what Imagen expects from your prompt. The result is a relightable 3D asset you can view from any angle, export as a mesh, or place in a scene.

Unlike pipelines that need large labeled 3D datasets, DreamFusion uses Score Distillation Sampling to turn a 2D diffusion prior into a 3D optimizer. That sidesteps the missing infrastructure for 3D denoising at scale. SDS alone gives reasonable appearance; DreamFusion adds regularizers for cleaner normals, depth, and surface geometry under Lambertian shading.

Researchers, 3D artists exploring generative workflows, and ML engineers studying text-to-3D use DreamFusion as the reference implementation from Google Research and UC Berkeley. The project page hosts a searchable gallery of hundreds of generated assets and cites the 2022 arXiv paper.