A list of synthetic dataset 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.
See also: http://riemenschneider.hayko.at/vision/dataset/index.php?filter=+synthetic
Click publication to jump to the paper title, detailed information such as code and project page will be provided together with pdf file.
## Outline This is a brief summary of this page, you can quickly jump to what you want. - [Synthetic image dataset](#dataset) - [3D models](#models) - [Tools](#tool) - [Resource Index](#resource) - [Reference](#reference) ## 1. Image dataset [↑](#outline)Name | Publication |
---|---|
Virtual KITTI | CVPR2016 |
Synthetia | CVPR2016 |
Sintel | ECCV2012 |
SceneFlow | CVPR2016 |
Realistic 3D models are critical for creating realistic and diverse virtual worlds. Here are research efforts for creating 3D model repositories.
Name | Publication |
---|---|
ShapeNet | ArXiv |
3dscan | ArXiv |
seeing3Dchairs | CVPR2014 |
Name | Platform | Publication |
---|---|---|
Render for CNN | Blender | ICCV2015 |
UETorch | Unreal Engine 4(UE4) | ICML2016 |
UnrealCV | UE4 | ArXiv |
VizDoom | Doom | ArXiv |
ECCV 2016 Virtual/Augmented Reality for Visual Artificial Intelligence (VARVAI) workshop link
Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments Siggraph Asia 2016 workshop link
Misc. ↑
- RealismCNN github
- Abnormality Detection in Images(http://paul.rutgers.edu/~babaks/abnormality_detection.html)
Related topics ↑
Great research always comes from great researchers. This is a short list of researchers that are combining computer vision with virtual worlds.
Reference ↑
If you want to edit this README file. The div id is bib citekey from google scholar, use div id makes it easier to reference a work in this document.
universe.openai.com
(Total=1)
- Aerial Informatics and Robotics Platform ([:octocat:code](https://github.com/Microsoft/AirSim)) ([pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/02/aerial-informatics-robotics-TR.pdf)) ([project](https://www.microsoft.com/en-us/research/project/aerial-informatics-robotics-platform/))(Total=14)
-
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning (pdf)
-
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
-
Procedural Generation of Videos to Train Deep Action Recognition Networks (pdf) (project)
-
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games (pdf) (code)
-
A Virtual Reality Platform for Dynamic Human-Scene Interaction. 2016
(pdf) (project) -
ResearchDoom and CocoDoom: Learning Computer Vision with Games. 2016
(pdf) (project)
-
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)
- Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning 2016
(pdf)
(Total=3)
- A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. 2015
(pdf) (citation:9)
(Total=2)
- Virtual and real world adaptation for pedestrian detection. 2014
(pdf) (citation:46)
(Total=1)
- Detailed 3d representations for object recognition and modeling. 2013
(pdf) (citation:67)
(Total=1)
- A naturalistic open source movie for optical flow evaluation. 2012 ([pdf](http://link.springer.com/chapter/10.1007/978-3-642-33783-3_44)) ([project](http://sintel.is.tue.mpg.de/)) ([citation:227](http://scholar.google.com/scholar?cites=15124407213489971559&as_sdt=20000005&sciodt=0,21&hl=en))(Total=1)
- Learning appearance in virtual scenarios for pedestrian detection. 2010
(pdf) (citation:79)
(Total=1)
- Ovvv: Using virtual worlds to design and evaluate surveillance systems. 2007
(pdf) (citation:58)