2016 |
Kravanja, Jaka; Žganec, Mario; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models Journal Article In: Sensors, vol. 16, no. 10, pp. 1740, 2016. Abstract | Links | BibTeX | Tags: 3d imaging, 3d sensor, depth imaging, depth sensor, graphical models, modulated pattern projection, outdoor deployment, robust operation, Sensors, structured light @article{kravanja2016robust,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. |
Kravanja, Jaka; Žganec, Mario; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir Exploiting Spatio-Temporal Information for Light-Plane Labeling in Depth-Image Sensors Using Probabilistic Graphical Models Journal Article In: Informatica, vol. 27, no. 1, pp. 67–84, 2016. Abstract | Links | BibTeX | Tags: 3d imaging, correspondance, depth imaging, depth sensing, depth sensor, graphical models, sensor, structured light @article{kravanja2016exploiting,This paper proposes a novel approach to light plane labeling in depth-image sensors relying on “uncoded” structured light. The proposed approach adopts probabilistic graphical models (PGMs) to solve the correspondence problem between the projected and the detected light patterns. The procedure for solving the correspondence problem is designed to take the spatial relations between the parts of the projected pattern and prior knowledge about the structure of the pattern into account, but it also exploits temporal information to achieve reliable light-plane labeling. The procedure is assessed on a database of light patterns detected with a specially developed imaging sensor that, unlike most existing solutions on the market, was shown to work reliably in outdoor environments as well as in the presence of other identical (active) sensors directed at the same scene. The results of our experiments show that the proposed approach is able to reliably solve the correspondence problem and assign light-plane labels to the detected pattern with a high accuracy, even when large spatial discontinuities are present in the observed scene. |