MagiScan vs WebODM

MagiScan is an AI-powered 3D scanning app and creation ecosystem for capturing real objects or generating models from text and photos. Its guide lists 360, Freeform, Hybrid, and Upload Photo capture modes. Models can be exported as OBJ, STL, FBX, PLY, USDZ, GLB, and GLTF. The maker lists workflows or integrations with Unity, Bambu Lab, Blender, Cura, PrusaSlicer, and OrcaSlicer. MagiScan Studio lets users browse scans on a computer and open models in supported 3D applications and slicers. A Unity plugin imports scans from a linked phone account into a project, and its panel loads the latest 500 scans. The app also includes an extension for sending scans to NVIDIA Omniverse and can turn scanned objects into Minecraft block structures; that feature is unofficial and not endorsed by Mojang AB. Getting started requires an iPhone or Android smartphone, and the maker says a smartphone camera is sufficient.

WebODM turns aerial images into georeferenced maps, point clouds, digital elevation models, textured 3D models, and Gaussian splats. It accepts photos or videos from any drone, including JPG, TIFF, video files, and multispectral imagery, and can run on a user's computer while offline. Exports include GeoTIFF, PNG, LAS/LAZ, OBJ, and OGC 3D Tiles. Mapping tools cover measurements, plant-health indexes, ground-control points, elevation contours, rolling-shutter correction, and sharing links. WebODM uses ODX, a free, open-source photogrammetry engine that developers can run from the command line or integrate into software. It can also connect to processing nodes that speak the NodeODX API. The API uses token authentication through the /api/token-auth/ endpoint, with tokens expiring after six hours by default. Model-level and row-level permissions govern whether users can view, add, edit, or delete objects. WebODM is free to install and use locally; optional cloud plans are listed at $24 a month billed yearly for Starter, $35 a month billed yearly for Pro, and $99 a month billed yearly for Business. Minimum requirements are 8 GB RAM and 20 GB disk space; larger datasets have higher recommended requirements.