Core Value Proposition
A cost-effective Visual Intelligence solution that transforms affordable sensor data (video, GPS, IMU) into actionable 3D/4D spatial intelligence through advanced SLAM and Gaussian splatting technologies—enabling organizations to digitize their physical assets without expensive LIDAR systems or pre-existing infrastructure.
Technology Architecture
Data Capture Layer
- Low-cost video cameras + GPS/IMU sensors
- Mobile collection platform (vehicle, backpack, or drone-mounted)
- Captures positional and visual data simultaneously
Processing Engine
- SLAM (Simultaneous Localization and Mapping): Solves the fundamental bootstrapping problem by:
- Building 3D maps from visual features while determining sensor position
- Enabling operation in GPS-denied environments (indoor, underground, urban canyons)
- Providing centimeter-level accuracy without external reference points
- 3D Gaussian Splatting: Converts multi-view imagery into:
- Real-time renderable 3D radiance fields
- Photorealistic scene reconstruction from novel viewpoints
- Temporal dimension capability for dynamic scene analysis
Output Deliverables
- Immersive 360° visual experiences
- Accurate 3D spatial models
- Time-series (4D) change detection
- Integration-ready formats for GIS, CAD, BIM platforms
Competitive Advantages
- Order-of-magnitude lower acquisition costs vs. traditional LIDAR-based Digital Twins
- GPS-independence enables coverage in challenging environments
- Real-time rendering via Gaussian splatting (vs. traditional photogrammetry workflows)
- Scalable deployment using consumer-grade hardware
- Rightshoring delivery model for cost-effective processing at scale
Target Applications
- Smart Cities: Infrastructure inventory, planning visualization
- Utilities: Asset inspection, network documentation in GPS-denied substations
- Transportation: HD mapping for autonomous systems, corridor monitoring
- Facilities Management: Indoor Digital Twins for large campuses, industrial sites
- Mining/Underground: Tunnel mapping, ventilation planning
Key Differentiator: While competitors require expensive LIDAR rigs, nfoldROI delivers comparable Digital Twin fidelity using affordable sensor packages, democratizing access to enterprise-grade spatial intelligence.
Above shows a video on the left taken by phone camera and examined by 4D-360 AI to determine the best conversion process to get to the 3D point cloud rendered on the right:
¨… A white Toyota Land Cruiser with roof rack and additional equipment parked on a quiet residential street. The vehicle is surrounded by suburban houses, mature trees, and well-maintained lawns. The frames show the camera orbiting around the vehicle, capturing front three-quarter, rear three-quarter, side, and various angled views. The lighting is natural daylight with some overcast conditions, providing even illumination without harsh shadows. The scene has good visual complexity with varied textures from the vehicle’s surfaces, foliage, and architectural elements in the background. Video: 1920×1080 @ 29.99fps, 48.55s (1456 frames)
The frames clearly show a systematic orbital motion around a white Toyota Land Cruiser stationed on a residential street. The camera maintains a consistent distance from the vehicle while capturing it from multiple angles – front-left, rear-left, rear-right, front-right, and returning to the starting position. This creates a complete 360-degree orbit around the stationary vehicle. The background environment (houses, trees, street) remains relatively stable with only perspective changes, indicating the camera is moving around the subject rather than through the scene. The vehicle remains the central focus throughout all frames, positioned consistently within the frame composition. The lighting appears natural and even, with good texture detail on both the vehicle and background elements. This orbital pattern with a stationary subject is ideally suited for … which excels at loop closure detection and accurate reconstruction of objects captured from multiple viewpoints in a circular pattern….¨
No LIDAR.
Again – a simple phone camera was used to capture this 3D Digital Twin.
