i.c.sens Stereo Image Vehicle Dataset

doi:doi:10.25835/0078519

i.c.sens Stereo Image Vehicle Dataset

The i.c.sens Stereo Image Vehicle Dataset is a dataset for vehicle detection, pose estimation, and reconstruction. It consists of street-level stereo images and delivers reference information for the 3D pose and shape of vehicles being visible in the scene. For the acquisition of the data, a vehicle was equipped with a synchronised and calibrated stereo camera rig and stereoscopic image sequences were recorded in urban environments in the city of Hanover for the generation of the data set. Vehicle CAD models are manually fitted to the observations and are provided as reference data.

Examples of the reference CAD models can be seen in the subsequent figure:

Details on the data and its usage can be found in the provided documentation file.

Related Publications:

Coenen, M. and Rottensteiner, F., 2019: Probabilistic Vehicle Reconstruction Using a Multi-Task CNN. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 822-831. https://ieeexplore.ieee.org/document/9022056
Schön, S., Brenner, C., Alkhatib, H., Coenen, M., Dbouk, H., Garcia-Fernandez, N., Fischer, C., Heipke, C., Lohmann, K., Neumann, I., Nguyen, U., Paffenholz, J.-A., Peters, T., Rottensteiner, F., Schachtschneider, J., Sester, M., Sun, L., Vogel, S., Voges, R. and Wagner, B., 2018: Integrity and Collaboration in Dynamic Sensor Networks. Sensors 18(7), pp. 2400-2421. https://www.mdpi.com/1424-8220/18/7/2400

BibTex:

@dataset{Max_Coenen_2020,
    abstract = {The _i.c.sens Stereo Image Vehicle Dataset_ is a dataset for vehicle detection, pose estimation, and reconstruction. It consists of street-level stereo images and delivers reference information for the 3D pose and shape of vehicles being visible in the scene. For the acquisition of the data, a vehicle was equipped with a synchronised and calibrated stereo camera rig and stereoscopic image sequences were recorded in urban environments in the city of Hanover for the generation of the data set. Vehicle CAD models are manually fitted to the observations and are provided as reference data.

Examples of the reference CAD models can be seen in the subsequent figure:
![Reference CAD Models](https://data.uni-hannover.de/dataset/df2eb5a1-e09f-422d-9dec-537f10d18a2a/resource/43941f67-663f-42ff-b604-298faa0ba754/download/labelsexamples.png "Visualisation of the reference CAD models")

Details on the data and its usage can be found in the provided __documentation__ file.

# __Related Publications:__
* __Coenen, M. and Rottensteiner, F., 2019__: Probabilistic Vehicle Reconstruction Using a Multi-Task
CNN. In: _IEEE International Conference on Computer Vision Workshops (ICCV Workshops)_,
pp. 822-831.
https://ieeexplore.ieee.org/document/9022056

* __Schön, S., Brenner, C., Alkhatib, H., Coenen, M., Dbouk, H., Garcia-Fernandez, N., Fischer, C.,
Heipke, C., Lohmann, K., Neumann, I., Nguyen, U., Paffenholz, J.-A., Peters, T., Rottensteiner,
F., Schachtschneider, J., Sester, M., Sun, L., Vogel, S., Voges, R. and Wagner, B., 2018:__ Integrity
and Collaboration in Dynamic Sensor Networks. _Sensors_ 18(7), pp. 2400-2421.
https://www.mdpi.com/1424-8220/18/7/2400},
    author = {Max Coenen},
    doi = {10.25835/0078519},
    institution = {i.c.sens},
    keyword = {'Pose Estimation', 'Stereo Images', 'Vehicle Detection', 'Vehicle Reconstruction'},
    month = {jul},
    publisher = {LUIS},
    title = {i.c.sens Stereo Image Vehicle Dataset},
    url = {https://service.tib.eu/ldmservice/vdataset/luh-icsens-stereo-image-vehicle-dataset},
    year = {2020}
}