The Science Behind Every Camera AI Breakthrough.
Five technology pillars — from raw photons to production-grade AI — built on 13 patents, 100+ years of combined expertise, and research published at SIGGRAPH, CVPR, and ECCV.
Technology Overview — Computational Imaging Pipeline Demo
Computational Imaging
Extracting what standard cameras miss.
Standard cameras capture visible-light RGB data. Lumirithmic's computational imaging pipelines use multi-spectral, polarised, and structured illumination to extract reflectance, depth, sub-surface scattering, and geometry data — turning any commodity camera into a precision scientific instrument.
| METHOD | Multi-spectral polarised illumination |
| CAPTURES | Reflectance · Depth · Geometry · Sub-surface |
| HARDWARE | Commodity cameras — no specialist rigs |
| RESOLUTION | 4K+ texture output |
| ACCURACY | ±0.1mm geometric precision |
- HDR and multi-spectral image acquisition
- Skin reflectance and sub-surface scattering capture
- Depth and surface geometry extraction
- Material and appearance property estimation
- Novel computational photography pipeline design
AI Model Architecture
Domain-specific intelligence over generic baselines.
Generic AI models are trained on generic data — they don't understand your environment, your imaging setup, or your domain constraints. Lumirithmic builds custom architectures trained on domain-specific datasets: face, skin, hair, material, motion, and object data specific to each client's use case.
| DOMAINS | Face · Skin · Hair · Material · Object |
| APPROACH | Neural rendering + classical geometry hybrid |
| TRAINING | Domain-specific — not generic web data |
| OUTPUT | 3D mesh · Texture · Appearance · Motion |
| VENUES | SIGGRAPH · CVPR · ICCV · ECCV |
- 3D face and body reconstruction from 2D inputs
- Skin reflectance and appearance estimation
- Facial expression and animation synthesis
- Material classification and recognition
- Foundation model development for specific domains
Edge AI
Full intelligence. No cloud dependency.
Large AI models can't run on consumer hardware out of the box. Lumirithmic's model optimisation pipeline — quantisation, pruning, and knowledge distillation — compresses production models for real-time on-device inference without sacrificing accuracy. We have deployed sub-second facial inference on standard consumer smartphones.
| TECHNIQUES | Quantisation · Pruning · Distillation |
| FORMATS | ONNX · CoreML · TensorRT · TFLite |
| LATENCY | Sub-second on consumer hardware |
| CLOUD REQUIRED | No — fully on-device |
| PRODUCTION REFERENCE | Consumer smartphones — millions of devices |
- Model compression without accuracy degradation
- Hardware-specific NPU optimisation
- Power-efficient inference for battery-constrained devices
- Real-time camera stream processing on-device
- Production deployment and benchmarking support
| Ingestion Layer | Camera streams · SDK uploads · REST API |
| Processing Cluster | Auto-scaling compute · GPU inference nodes |
| Model Serving | Version-controlled model registry · A/B testing |
| Output & Storage | Results API · GDPR-compliant data store |
Cloud Vision Pipelines
Scale to millions. Process in parallel.
When edge deployment isn't sufficient — or when you need to scale to millions of concurrent scans — Lumirithmic builds cloud-native vision pipelines. Real-time streams and async batch modes, auto-scaling infrastructure, GDPR-compliant storage, and full API access to processed results.
| SCALE | Millions of concurrent scans |
| MODES | Real-time stream + async batch |
| INTEGRATION | REST API · Webhooks · Event streams |
| INFRASTRUCTURE | Auto-scaling · Blue-green deploys |
| COMPLIANCE | GDPR · SOC 2 · Data residency |
- End-to-end pipeline design and deployment
- Real-time camera stream ingestion and processing
- Model serving infrastructure with version control
- Monitoring, alerting, and auto-rollback
- GDPR-compliant biometric data handling and storage
Camera Calibration & Capture Systems
Precision hardware meets proprietary software.
AI models are only as good as the data feeding them. Lumirithmic designs and calibrates the capture systems that generate that data — from single-camera smartphone setups to 360° multi-camera domes — handling geometric, photometric, and radiometric calibration to ensure every pixel is a reliable data point.
| SYSTEMS | Domes · Phone arrays · Desktop rigs |
| CALIBRATION TYPES | Geometric · Photometric · Radiometric |
| SYNC PRECISION | Sub-millisecond multi-camera trigger |
| COMPATIBILITY | Multi-vendor camera support |
| DELIVERY | Concept → Prototype → Production |
- Custom multi-camera dome design and assembly
- Sub-millisecond synchronised trigger systems
- Geometric and radiometric calibration pipelines
- Smartphone and commodity camera array setups
- Hardware-software co-design for imaging applications
The Full Technology Stack
| Technology | Layer | Key Output | Best Applied In |
|---|---|---|---|
| Computational Imaging | Capture | Raw data streams | Beauty · Film · Research |
| AI Model Architecture | Intelligence | Domain-specific models | All verticals |
| Edge AI | Deployment | On-device inference | Mobile · Consumer |
| Cloud Vision Pipelines | Scale | API-accessible results | Enterprise · SaaS |
| Calibration & Capture Systems | Hardware | Calibrated image streams | Studios · R&D · Enterprise |
Standard cameras capture only visible-light RGB data. Our computational imaging pipelines use multi-spectral, polarised, and structured illumination to extract reflectance, sub-surface, depth, and geometry data invisible to standard sensors — turning any camera into a rich data instrument.
No. Every model is trained on proprietary domain-specific datasets captured with our own imaging pipelines — face, skin, hair, material, and object data collected under controlled conditions. This gives our models a data advantage that generic web-trained models cannot match.
Yes. We have deployed sub-second facial inference on standard consumer smartphones using quantisation, pruning, and knowledge distillation. Models run fully on-device with no cloud dependency, supporting ONNX, CoreML, TensorRT, and TFLite formats.
End-to-end pipeline design and deployment: ingestion layer for camera streams and SDK uploads, auto-scaling compute cluster with GPU inference nodes, version-controlled model serving with A/B testing, and a GDPR-compliant output store with REST API access.
Our calibration and capture systems support commodity cameras — no specialist rigs required for most use cases. For multi-camera dome and array setups we work with any vendor, providing geometric, photometric, and radiometric calibration for the full rig.
Calibration engagements typically run 2–6 weeks depending on rig complexity. Full system integrations — from concept to production-ready capture system — are scoped individually. We follow a Concept → Prototype → Production delivery model with clear milestones at each stage.
Yes. Domain-specific fine-tuning is a core part of how we work. We either use your labelled data directly or run it through our annotation pipeline. Fine-tuning is available for all model types including detection, segmentation, appearance estimation, and 3D reconstruction.
Yes. Edge AI deployments are fully air-gapped by design — no cloud dependency. For cloud vision pipelines we also offer private cloud and on-premise infrastructure options, with full data residency controls and compliance with GDPR and SOC 2 requirements.
