Torr Vision Group (torrvision)

Torr Vision Group

torrvision

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Torr Vision Group at the Department of Engineering Science of the University of Oxford, led by Prof. Philip Torr

Location:Oxford, United Kingdom

Home Page:http://www.robots.ox.ac.uk/~tvg

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Torr Vision Group's repositories

crfasrnn

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

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crayon

A language-agnostic interface to TensorBoard

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siamfc-tf

SiamFC tracking in TensorFlow.

Objectness

BING Objectness proposal estimator linux/mac/windows version implementation, runs at 1000 FPS. More in http://mmcheng.net/bing/ and also http://www.robots.ox.ac.uk/~szheng/DepthProposals.html

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spaint

A framework for interactive, real-time 3D scene segmentation

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focal_calibration

Code for the paper "Calibrating Deep Neural Networks using Focal Loss"

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pyORBSLAM2

Ultra-fast Boost.Python interface for ORBSLAM2

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CollaborativeSLAMDataset

The dataset associated with our ISMAR 2018 paper on Collaborative SLAM.

caffe

Caffe fork that supports CRF-RNN

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straighttoshapes

Straight To Shapes: Real-Time Detection of Encoded Shapes

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ELAS_SoC

Source Code for "Real-Time Dense Stereo Matching with ELAS on FPGA Accelerated Embedded Devices"

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whoneedsadversaries

Code implementing the experiments described in the NeurIPS 2018 paper "With Friends Like These, Who Needs Adversaries?".

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caffe-tvg

Caffe fork supporting segmentation algorithms by the Torr Vision Group

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ViZDoom

Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information. :godmode:

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logger

A simple logger for experiments

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AirSim

Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research

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BicycleGAN

[NIPS 2017] Toward Multimodal Image-to-Image Translation

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doom-py

ViZDoom Python wrapper

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DSGAN

Implementation of Diversity-Sensitive Conditional Generative Adversarial Networks (ICLR 2019)

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gym-doom

Gym - Doom environments based on VizDoom.

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malmo

Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment. --- For installation instructions, scroll down to *Getting Started* below, or visit the project page for more information:

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Mobile-SDK-Android

DJI Mobile SDK for Android: http://developer.dji.com/mobile-sdk/

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octomap_mapping

ROS stack for mapping with OctoMap, contains octomap_server package

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octomap_rviz_plugins

RViz display plugins for visualizing octomap messages in ROS

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opencv

Open Source Computer Vision Library

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pytorch-mace

A pytorch implementation of Structured Exploration via Deep Hierarchical Coordination

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sacred

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

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