Projects
Projects
Software
CodeSLAM
A system that generates large scale photorealistic rendering of indoor scene trajectories.
DeepFactors
A real-time dense visual SLAM system capable of capturing comprehensive dense keyframe maps of room scale environments explored using an RGB camera.
ElasticFusion
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.
MoreFusion
A real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.
ReCo
A contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
SceneNet RGB-D
A system that generates large scale photorealistic rendering of indoor scene trajectories.
SemanticFusion
A real-time visual SLAM system capable of semantically annotating a dense 3D scene using Convolutional Neural Networks.
X-Section
An RGB-D 3D reconstruction approach that leverages deep learning to make object-level predic- tions about thicknesses that can be readily integrated into a volumetric multi-view fusion process.
Datasets
RLBench Dataset
RLBench features 400 variations of 100 completely unique, hand designed tasks ranging in difficulty, from simple target, such as reaching and door opening, to longer multi-stage tasks, such as opening an oven and placing a tray in it. The scale and diversity of RLBench offers unparalleled research opportunities in the robot learning community and beyond.
SceneNet RGB-D
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.