A list of synthetic dataset and tools for computer vision
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README.md

Synthetic for Computer Vision

This is a repo for tracking the progress of using synthetic images for computer vision research. If you found any important work is missing or information is not up-to-date, please edit this file directly and make a pull request. Each publication is tagged with a keyword to make it easier to search.

If you find anything missing from this page, please edit this README.md file to add it. When adding a new item, you can simply follow the format of existing items. How this document is structured is documented in contribute.md.

How to use: Click publication to jump to the paper title, detailed information such as code and project page will be provided together with pdf file.**

Synthetic image dataset

3D Model Repository

Realistic 3D models are critical for creating realistic and diverse virtual worlds. Here are research efforts for creating 3D model repositories.

Tools

Resources

ECCV 2016 Virtual/Augmented Reality for Visual Artificial Intelligence (VARVAI) workshop

Role of Simulation in Computer Vision

Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments Siggraph Asia 2016 workshop

See also: http://riemenschneider.hayko.at/vision/dataset/index.php?filter=+synthetic

Misc.

Reference

2017

(Total=3)

  • Nvidia Issac

  • Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes

  • Aerial Informatics and Robotics Platform (:octocat:code) (pdf) (project) tag: tool
  • Tobin, Josh, et al. "Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World." arXiv preprint arXiv:1703.06907 (2017). tag: domain (pdf)

2016

(Total=17)

  • Sadeghi, Fereshteh, and Sergey Levine. "rl: Real single-image flight without a single real image. arXiv preprint." arXiv preprint arXiv:1611.04201 12 (2016). tag: rl

  • Johnson, Justin, et al. "CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning." arXiv preprint arXiv:1612.06890 (2016). (pdf)

  • McCormac, John, et al. "SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth." arXiv preprint arXiv:1612.05079 (2016).

  • de Souza, César Roberto, et al. "Procedural Generation of Videos to Train Deep Action Recognition Networks." arXiv preprint arXiv:1612.00881 (2016). (pdf) (project) tag: synthetic human

  • Synnaeve, Gabriel, et al. "TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games." arXiv preprint arXiv:1611.00625 (2016). (pdf) (code)

  • Lin, Jenny, et al. "A virtual reality platform for dynamic human-scene interaction." SIGGRAPH ASIA 2016 Virtual Reality meets Physical Reality: Modelling and Simulating Virtual Humans and Environments. ACM, 2016. (pdf) (project)

  • Mahendran, A., et al. "ResearchDoom and CocoDoom: Learning Computer Vision with Games." arXiv preprint arXiv:1610.02431 (2016). (pdf) (project)

  • The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes. 2016 (pdf) (project) (citation:4)
  • Virtual Worlds as Proxy for Multi-Object Tracking Analysis. 2016
    (pdf) (project) (citation:5)

  • Playing for data: Ground truth from computer games. 2016
    (pdf) (citation:1)

  • Play and Learn: Using Video Games to Train Computer Vision Models. 2016
    (pdf) (citation:1)

  • ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning. 2016
    (:octocat:code) (pdf) (project) (citation:4)

  • UnrealCV: Connecting Computer Vision to Unreal Engine 2016
    (:octocat:code) (project) (pdf)
  • Learning Physical Intuition of Block Towers by Example 2016
    (:octocat:code) (pdf) (citation:12)

  • Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning 2016
    (pdf)

  • A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields. ACCV 2016
    (:octocat:code) (pdf) (project) (citation)

2015

(Total=3)

  • A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. 2015
    (pdf) (citation:9)
  • Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views. 2015
    (:octocat:code) (pdf) (citation:33)

2014

(Total=2)

  • Virtual and real world adaptation for pedestrian detection. 2014
    (pdf) (citation:46)
  • Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models. 2014
    (:octocat:code) (pdf) (project) (citation:110)

2013

(Total=1)

  • Detailed 3d representations for object recognition and modeling. 2013
    (pdf) (citation:67)

2012

(Total=1)

2010

(Total=1)

  • Learning appearance in virtual scenarios for pedestrian detection. 2010
    (pdf) (citation:79)

2007

(Total=1)

  • Ovvv: Using virtual worlds to design and evaluate surveillance systems. 2007
    (pdf) (citation:58)