🔬 Research Implementation Capabilities

From Research Papers
To Production Systems

We don't just wrap APIs—we implement cutting-edge computer vision research from CVPR, ICCV, and ECCV, adapting novel techniques to solve your specific problems.

Research Areas We Implement

State-of-the-art techniques adapted for commercial deployment

3D Reconstruction & Novel View Synthesis

  • Neural Radiance Fields (NeRF) variants
  • 3D Gaussian Splatting for real-time rendering
  • Multi-view stereo and SfM pipelines
  • Instant-NGP and efficient variants

Foundation Models & Adaptation

  • Segment Anything (SAM) fine-tuning
  • CLIP and vision-language models
  • Domain-specific model adaptation
  • Few-shot and zero-shot learning

Custom Detection & Segmentation

  • Modified YOLO architectures for edge
  • U-Net, nnU-Net, TransUNet variants
  • Mask R-CNN and instance segmentation
  • Panoptic and semantic segmentation

Medical Imaging AI

  • Organ and lesion segmentation networks
  • Medical image registration techniques
  • Multi-modal fusion (CT/MRI/PET)
  • Pathology slide analysis models

Visual Transformers & Attention

  • Vision Transformers (ViT) for classification
  • DETR and transformer-based detection
  • Self-attention mechanisms for CV
  • Swin Transformers and variants

Video Analysis & Tracking

  • Multi-object tracking (MOT) algorithms
  • Action recognition and video understanding
  • Optical flow and motion estimation
  • Real-time video segmentation

Recent Research Implementations

Real-world results from cutting-edge papers

🎯

Segment Anything Model (SAM) Adaptation

Adapted Meta's SAM foundation model for medical image segmentation in CT scans. Fine-tuned on custom dataset of 5,000 annotated volumes.

15% Accuracy Improvement
Over base SAM model
3 Weeks to Production
From paper to deployment
🌐

3D Gaussian Splatting for Construction

Implemented real-time 3D reconstruction from drone footage for construction site monitoring using latest Gaussian Splatting techniques.

60fps Real-Time Rendering
On consumer hardware
Sub-cm Accuracy
Volume measurements

Custom YOLO Architecture for Industrial Inspection

Modified YOLOv8 architecture with attention mechanisms for defect detection on manufacturing lines. Optimized for NVIDIA Jetson deployment.

3x Faster Inference
vs. standard YOLOv8
98.5% Defect Detection
In production environment

Our Research-to-Production Process

How we go from paper to deployed system

1

Paper Analysis & Feasibility

We review the latest papers from CVPR, ICCV, ECCV, and arXiv to identify techniques applicable to your problem. We assess computational requirements, data needs, and potential performance gains.

2

Clean Implementation

We implement the algorithm from scratch in production-ready code (PyTorch, TensorFlow). No copy-paste from research repos—we build for maintainability and deployment from day one.

3

Domain Adaptation

We adapt the architecture to your specific data, hardware constraints, and performance requirements. This includes custom loss functions, data augmentation, and architectural modifications.

4

Optimization & Deployment

We optimize for your target platform (edge devices, cloud, mobile). This includes quantization, pruning, TensorRT conversion, and custom CUDA kernels when needed for real-time performance.

5

Validation & Documentation

Rigorous testing against benchmarks and real-world data. We provide comprehensive documentation including architecture decisions, hyperparameters, and deployment guides.

Academic Partnerships & Research Access

Located next to Purdue University's CS department

🎓 Research Access

Direct access to cutting-edge research from top-tier universities. We stay current with the latest developments before they hit mainstream tools.

🤝 Collaborative Development

When needed, we collaborate with academic researchers to adapt their work for commercial deployment, ensuring techniques are properly implemented.

📚 Continuous Learning

Our team regularly reviews papers from major conferences (CVPR, ICCV, ECCV, NeurIPS) to identify techniques for client applications.

🔬 PhD-Led Implementation

Led by Dr. Tharindu Mathew (Purdue PhD, Ex-Microsoft Research), ensuring research is properly understood and implemented.

Need a Custom Research Implementation?

Whether it's a specific paper you want implemented or a novel technique you need developed, let's discuss how we can bring cutting-edge research to your production system.

Schedule Research Consultation →