Download SYNTHIA-CVPR’16

CITATION
When using or referring to this SYNTHIA-CVPR’16 in your research, please cite our CVPR 2016 paper [ pdf ], please check our terms of use.

German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, Antonio M. Lopez; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3234-3243

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@InProceedings{Ros_2016_CVPR,
author = {Ros, German and Sellart, Laura and Materzynska, Joanna and Vazquez, David and Lopez, Antonio M.},
title = {The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

 

 

RELATED VIDEOS

SYNTHIA dataset: Depth groundtruth (Highway sequence)
SYNTHIA dataset: Senatic Segmentation Groundtruth (New York sequence)
SYNTHIA dataset: Semantic Segmentation groundtruth (Highway sequence)
SYNTHIA dataset: RGB (New York Snow sequence)
SYNTHIA dataset: RGB (New York SunSet sequence)
SYNTHIA at news of Tomorrow daily at CNET

randcvpr2016

SYNTHIA-RAND-CVPR16

It is the set containing the original 13,407 images used to perform training and domain adaptation of the models presented in our CVPR’16 paper. These images are generated as random perturbation of the world and therefore do not have temporal consistency (this is not a video stream). These images have annotations for 11 basic classes and do not have annotations for instances. The classes are: void, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist.

randcityscapes

SYNTHIA-RAND-CITYSCAPES

It is a new set containing 9,000 random images with labels compatible with the CITYSCAPES test set. In addition to the CITYSCAPES test classes, we also provide other interesting ones such as lanemarking. The list of classes is: void, sky, building, road, sidewalk, fence, vegetation, pole, car, traffic sign, pedestrian, bicycle, motorcycle, parking-slot, road-work, traffic light, terrain, rider, truck, bus, train, wall, lanemarking. These images are generated as random perturbation of the virtual world, therefore no temporal consistency is provided (this is not a video stream). This set contains groundtruth for instances!

SYNTHIA VIDEO SEQUENCES

Video subsets acquired at 5 FPS. There are 7 sequences featuring different scenarios and traffic conditions. Each of them is divided into different sub-sequences for commodity. Each sub-sequence consists of the same traffic situation but under a different weather/illumination/season condition. The current sub-sequences are: Spring, Summer, Fall, Winter, Rain, Soft-rain, Sunset, Fog, Night and Dawn. Each of these sub-sequences contains around 8,000 (1,000 x 8) images with associated ground truth. For each sub-sequence we provide useful information such as: 8 views, ground truth for semantic segmentation, instance segmentation, global camera poses, depth, and calibration parameters. In this case the semantic classes are 13: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking.

i01

SEQS-01 (Highway)

 Sequence featuring a highway scenario.

Available sub-sequences: Fall, Winter, Summer, Dawn

 

SEQS02

SEQS-02 (NewYork-like city)

 Sequence featuring a New York-like city.

Available sub-sequences: Fall, Winter, Summer, Night, Dawn, Rain

i03

SEQS-03 (Roundabout)

 Sequence featuring a roundabout scenario.

Available sub-sequences: –

 

i04

SEQS-04 (Old European Town)

 Sequence featuring an old European town.

Available sub-sequences: Fall, Winter, Summer, Sunset, Rain

 

i05

SEQS-05 (NewYork-like city)

 Sequence featuring a New York-like city.

Available sub-sequences: Fall, Winter, Summer, Rain, Sunset

 

i06

SEQS-06 (Highway)

 Sequence featuring a highway scenario.

Available sub-sequences: Fall, Summer, Night, Sunset

 

i07

SEQS-07 (Mountain Path)

 Sequence featuring an old European town.

Available sub-sequences: Fall, Summer, Rain, Sunset

 


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SYNTHIA-RAND-CVPR2016
SYNTHIA-RAND-CITYSCAPES
SYNTHIA-SEQS-01-DAWN
SYNTHIA-SEQS-01-FALL
SYNTHIA-SEQS-01-FOG
SYNTHIA-SEQS-01-NIGHT
SYNTHIA-SEQS-01-SPRING
SYNTHIA-SEQS-01-SUMMER
SYNTHIA-SEQS-01-SUNSET
SYNTHIA-SEQS-01-WINTER
SYNTHIA-SEQS-01-WINTERNIGHT
SYNTHIA-SEQS-02-DAWN
SYNTHIA-SEQS-02-FALL
SYNTHIA-SEQS-02-FOG
SYNTHIA-SEQS-02-NIGHT
SYNTHIA-SEQS-02-RAINNIGHT
SYNTHIA-SEQS-02-SOFTRAIN
SYNTHIA-SEQS-02-SPRING
SYNTHIA-SEQS-02-SUMMER
SYNTHIA-SEQS-02-SUNSET
SYNTHIA-SEQS-02-WINTER
SYNTHIA-SEQS-04-DAWN
SYNTHIA-SEQS-04-FALL
SYNTHIA-SEQS-04-FOG
SYNTHIA-SEQS-04-NIGHT
SYNTHIA-SEQS-04-RAINNIGHT
SYNTHIA-SEQS-04-SOFTRAIN
SYNTHIA-SEQS-04-SPRING
SYNTHIA-SEQS-04-SUMMER
SYNTHIA-SEQS-04-SUNSET
SYNTHIA-SEQS-04-WINTER
SYNTHIA-SEQS-04-WINTERNIGHT
SYNTHIA-SEQS-05-DAWN
SYNTHIA-SEQS-05-FALL
SYNTHIA-SEQS-05-FOG
SYNTHIA-SEQS-05-NIGHT
SYNTHIA-SEQS-05-RAIN
SYNTHIA-SEQS-05-RAINNIGHT
SYNTHIA-SEQS-05-SOFTRAIN
SYNTHIA-SEQS-05-SPRING
SYNTHIA-SEQS-05-SUMMER
SYNTHIA-SEQS-05-SUNSET
SYNTHIA-SEQS-05-WINTER
SYNTHIA-SEQS-05-WINTERNIGHT
SYNTHIA-SEQS-06-DAWN
SYNTHIA-SEQS-06-FOG
SYNTHIA-SEQS-06-NIGHT
SYNTHIA-SEQS-06-NIGHT
SYNTHIA-SEQS-06-SPRING
SYNTHIA-SEQS-06-SUMMER
SYNTHIA-SEQS-06-SUNSET
SYNTHIA-SEQS-06-WINTER
SYNTHIA-SEQS-06-WINTERNIGHT

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