Projects

source_code

Software


CodeSLAM

A system that generates large scale photorealistic rendering of indoor scene trajectories.

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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.

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SceneNet RGB-D

A system that generates large scale photorealistic rendering of indoor scene trajectories.

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SemanticFusion

A real-time visual SLAM system capable of semantically annotating a dense 3D scene using Convolutional Neural Networks.

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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.

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dataset

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. 

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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.

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