Computer Vision & Perception
State-of-the-art algorithms for object detection, semantic segmentation, 3D reconstruction, and scene understanding — enabling machines to interpret the visual world.
Computer Vision & Perception
Our computer vision research pushes the boundaries of visual intelligence — from real-time object detection in low-resource environments to full 3D scene reconstruction. We develop transformer-based architectures and lightweight inference pipelines suited to edge deployment in African contexts where compute is constrained.
Key Research Topics
Object Detection
Real-time multi-class detection using YOLO-family and transformer-based detectors, optimised for low-power embedded systems.
Scene Segmentation
Pixel-level semantic and panoptic segmentation for autonomous driving, medical imaging, and satellite scene parsing.
3D Reconstruction
Neural Radiance Fields (NeRF) and photogrammetry pipelines for high-fidelity 3D scene and object reconstruction.
Edge Inference
Quantised, pruned models and TensorRT deployment strategies enabling full vision pipelines on edge and mobile hardware.
Current Projects
EdgeSight: Low-Power Detection Framework
Low-power object detection framework enabling real-time computer vision on edge devices with minimal energy consumption.
Scene3D: Neural Radiance Field Reconstruction
Neural radiance field reconstruction system for high-fidelity 3D scene modelling from 2D image collections.
Related Publications
Interested in Collaborating?
We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.