A system that generates large scale photorealistic rendering of indoor scene trajectories.
A real-time visual SLAM system capable of semantically annotating a dense 3D scene using Convolutional Neural Networks.
A real-time dense visual SLAM system capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera.
BRISK - Binary Robust Invariant Scalable Keypoints
Developed by Dr Letenegger independent of Dyson funding, this package allows for high-speed scale-space keypoint detection, description and matching with options on invariance to in-plane rotation and scale change.
Large scale photorealistic rendering of indoor scene trajectories. Random sampling permits virtually unlimited scene configurations, and here we provide a set of 5M rendered RGB-D images from over 15K trajectories in synthetic layouts with random but physically simulated object poses. Each layout also has random lighting, camera trajectories, and textures.