- Datasets
Datasets
Create datasets to upload and publish data. Further organize your data using folders and assign metadata at both the file and dataset level.
STARC Repository
http://public.cyi.ac.cy/starcRepo/
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CHERE - Structure from Motion tools
A collection of tools and containers to enable simpler structure from motion reconstruction for end users.
The container can reconstruct 3D scene from a series of photos of a scene
by utilizing Structure from Motion (SfM).
Main script is at /sfm/sfm and it is a shell script that does the following:
- VisualSFM (ccwu.me/vsfm) is used for feature detection and matching as well
as reconstruction.
- SIFT - by using vlfeat (www.vlfeat.org)
- Yasutaka Furukawa's PMVS/CMVS (grail.cs.washington.edu/software/cmvs/)
- Screened Poisson Surface Reconstruction (http://www.cs.jhu.edu/~misha/Code/PoissonRecon)
- texrecon (https://github.com/nmoehrle/mvs-texturing)
- meshlab (www.meshlab.net)
- Nexus by Visual Computing Laboratory - ISTI - CNR (http://vcg.isti.cnr.it/nexus)
The container expects images in /input directory and will save output to
/output directory. It will produce sparse and dense reconstruction as well
as textured models in OBJ and PLY formats as well as NXS format suitable for streaming.
You can use binding mount or volumes, etc. for /input and /output. For example:
docker run --mount type=bind,source=/path/to/images,target=/input --mount type=bind,source=/path/to/output,target=/output sfm:latest /sfm/sfm
or
docker run -ti -v $(pwd)/input:/input -v $(pwd)/output:/output sfm:latest /sfm/sfm
This container was produced in EU H2020 project VI-SEEM (www.vi-seem.eu)
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Students
Students in the University of Tel Aviv, Israel.
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Zara
Crowds in from of the zara shop in Makariou Street, Nicosia, Cyprus.
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Arxiepiskopi
Crowd datasets of pedestrians. These include people moving in commercial streets, a school and tourist attractions.
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Trained convnet models
Convnet models trained from scratch on a HRRS image dataset.
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The container can reconstruct 3D scene from a series of photos of a scene
by utilizing Structure from Motion (SfM).
Main script is at /sfm/sfm and it is a shell script that does the following:
- VisualSFM (ccwu.me/vsfm) is used for feature detection and matching as well
as reconstruction.
- SIFT - by using vlfeat (www.vlfeat.org)
- Yasutaka Furukawa's PMVS/CMVS (grail.cs.washington.edu/software/cmvs/)
- Screened Poisson Surface Reconstruction (http://www.cs.jhu.edu/~misha/Code/PoissonRecon)
- texrecon (https://github.com/nmoehrle/mvs-texturing)
- meshlab (www.meshlab.net)
- Nexus by Visual Computing Laboratory - ISTI - CNR (http://vcg.isti.cnr.it/nexus)
The container expects images in /input directory and will save output to
/output directory. It will produce sparse and dense reconstruction as well
as textured models in OBJ and PLY formats as well as NXS format suitable for streaming.
You can use binding mount or volumes, etc. for /input and /output. For example:
docker run --mount type=bind,source=/path/to/images,target=/input --mount type=bind,source=/path/to/output,target=/output sfm:latest /sfm/sfm
or
docker run -ti -v $(pwd)/input:/input -v $(pwd)/output:/output sfm:latest /sfm/sfm
This container was produced in EU H2020 project VI-SEEM (www.vi-seem.eu)
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