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@article{kravanja2016robust,

title = {Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models},

author = {Jaka Kravanja and Mario Žganec and Jerneja Žganec-Gros and Simon Dobrišek and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/en/robustdepthimageacquisitionusingmodulatedpatternprojectionandprobabilisticgraphicalmodels-2/},

doi = {10.3390/s16101740},

year  = {2016},

date = {2016-10-20},

urldate = {2016-10-20},

journal = {Sensors},

volume = {16},

number = {10},

pages = {1740},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors.},

keywords = {3d imaging, 3d sensor, depth imaging, depth sensor, graphical models, modulated pattern projection, outdoor deployment, robust operation, Sensors, structured light},

pubstate = {published},

tppubtype = {article}

}