ScanTo3D | OpalAi vs Text-To-4D

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.

Text-To-4D, also known as MAV3D (Make-A-Video3D), generates three-dimensional dynamic scenes from simple text descriptions. It uses a 4D dynamic Neural Radiance Field (NeRF) optimized for consistent scene appearance, density, and motion by leveraging a Text-to-Video diffusion model. This allows the creation of dynamic videos that can be viewed from any camera angle and integrated into various 3D environments.

Unlike traditional 3D generation methods, MAV3D does not require any 3D or 4D training data. Instead, it relies on a Text-to-Video model trained solely on text-image pairs and unlabeled videos, making it accessible for users without specialized datasets. This approach opens up new possibilities for creators, developers, and researchers interested in generating immersive 3D dynamic content from text prompts.

The tool is designed for a broad audience including game developers, animators, and virtual reality content creators who want to quickly produce dynamic 3D scenes without manual modeling or animation. It offers a unique value by combining text-driven generation with 3D dynamic scene output, which can be used in interactive applications or visual storytelling.

Technically, the method integrates a 4D NeRF with a diffusion-based Text-to-Video model to ensure motion and appearance consistency over time and space. This results in smooth, realistic dynamic scenes that can be explored from multiple viewpoints. The system improves upon previous internal baselines by producing higher quality and more coherent 3D videos from textual input.

Overall, Text-To-4D stands out as the first known method to generate fully dynamic 3D scenes from text, bridging the gap between text-based video generation and 3D scene synthesis. It offers a flexible and innovative solution for creating immersive content without the need for complex 3D data or manual animation.