Early Stage Researcher (M/F/D)
in the Marie Skłodowska-Curie Innovative Training Network Project MENELAOSNT in the field
Deep Depth from Defocus (Deep DFD) for near range and in-situ 3D exploration (ESR05)
Acquiring 3D geometry of the scene is essential for many applications in the areas of navigation, robotics, scene understanding, etc. Among the existing approaches, those using passive devices are of increased interest since they allow the use of compact, standard and low cost imaging systems like DSLR cameras. There are many depth cues that can be used to extract the 3D geometry. In single shot images, the depth is laying in the blur, shadows of objects, chromatic effects and shape distortions caused by lens aberrations, etc. When multiple images are used, depth information comes from perspective change like in binocular systems or structures motion in video sequences. The physics of these effects is well known and more or less accurate mathematical models exist and are used by analytical image processing methods that are generally prone to heavy calculation.
The entrance of the newcoming Deep Neural Networks (DNN) on the stage of signal processing has boosted the subject due to their capability to learn complex models that ingest multiple effects, not only single ones as analytical approaches are doing. The flexibility in learning and the fast processing once the training is accomplished make DNNs a very promising tool in building the 3D geometry of scenes from easy-to-acquire images.
The successful candidate will be employed for 3 years by University Politehnica of Bucharest and will be part of CEOSpaceTech, a very dynamic research laboratory oriented towards space applications. She or he will receive a financial package, which is twice the average salary in the country and additional mobility and family allowance, granted according to the rules for Early Stage Researchers (ESRs) in the EU Marie Skłodowska-Curie Actions Innovative Training Networks (ITN). A career development plan will be prepared for her/him in accordance with the supervisor. The plan will include a choice of more than 20 streamed or registered courses, stays in Spain, and various outreach activities. For more information, please visit the Marie Skłodowska-Curie Actions Innovative Training Networks website.
- Study of physical foundation for depth cues in images and evaluation of their potential in existing methods for depth mapping.
- Elaboration of DNN-based solutions for depth inference from single shot images by exploiting defocus and other depth cues.
- Definition of benchmarks for DNN training, validation and testing.
- Evaluation of the accuracy of depth maps obtained with the DNNs using indoor and outdoor image collection.
- A Master of Science in Computer Science is required. It could comprise to the full range of mathematical, physical, engineering and technology disciplines related to sensor data acquisition and programming.
- Proficient English level
- Image analysis, neural networks, programming languages, basic knowledge on optics would be an advantage.
- CiTIUS, Santiago de Compostela, Spain, Prof. Dr. P. López Martínez, 3 months, study the possibilities to integrate the coded aperture into the sensor and to understand the impact of sensor technology on the camera performance.
- INSITU, Santiago de Compostela, Spain, Prof. Dr. P. Arias Sánchez, 5 months, refine the camera specification by studying the applications.
- [Gro] Grossmann, Paul. “Depth from focus”. Pattern Recognition Letters 5, no. 1 (1987): 63-69.
- [Lev] Levin, Anat, Rob Fergus, Frédo Durand, and William T. Freeman. “Image and depth from a conventional camera with a coded aperture”. In ACM Transactions on Graphics (TOG), vol. 26, no. 3, p. 70. ACM, 2007.
- [Lin] Lin, Jingyu, et al. “Separable coded aperture for depth from a single image.” IEEE Signal Processing Letters 21.12 (2014): 1471-1475.
- [Zho] Zhou, Changyin, Stephen Lin, and Shree K. Nayar. “Coded aperture pairs for depth from defocus and defocus deblurring.” International journal of computer vision 93.1 (2011): 53-72.
- [Eig] Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. NIPS (2014)
- [Liu] Liu, Fayao, Chunhua Shen, and Guosheng Lin. “Deep convolutional neural fields for depth estimation from a single image.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
- [Car] Carvalho, Marcela, et al. “Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?” Proceedings of the European Conference on Computer Vision (ECCV). 2018.
- [NYU] https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
University POLITEHNICA of Bucharest is the oldest and most prestigious engineer school in Romania. Its traditions are related to the establishment, in 1818, by Gheorghe Lazăr, of the first higher technical school with teaching in Romanian, at the Saint Sava Abbey in Bucharest. In 1832, it was reorganized into the St. Sava College. On 1 October 1864, The School of Bridges and Roads, Mines and Architecture was established, which becomes, on 30 October 1867, The School of Bridges, Roads and Mines, with a duration of 5 years. Under the leadership of Gheorghe Duca, on 1 April 1881, the institution acquires a new structure, under the name of The National School of Bridges and Roads; on 10 June 1920, the Politehnica School of Bucharest was founded, with four departments: Electromechanics, Civil Engineering, Mines and Metallurgy, Industrial Section. From November 1920 the name changes to POLITEHNICA of Bucharest. On 3 August 1948, the Polytechnic Institute of Bucharest was founded, which initially included 4 faculties and in which, since 1950, have appeared most of the current faculties. Based on the resolution of the Senate of November 1992, the Polytechnic Institute of Bucharest became University POLITEHNICA of Bucharest.
The position will be located at
University Politehnica of Bucharest
Splaiul Independenței nr 313, Sector 6
060042 București, Romania
Supervisor: Prof. Dr. Daniela Coltuc
Planned Recruitment date: 1st September 2020.
Eligibility Criteria and Mobility Rule
ESRs must, at the date of recruitment, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree. Full-Time Equivalent Research Experience is measured from the date when the researcher obtained the first degree entitling him/her to embark on a doctorate (either in the country in which the degree was obtained or in the country in which the researcher is recruited), even if a doctorate was never started or envisaged. Researchers can be of any nationality.
ESRs must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting beneficiary for more than 12 months in the 3 years immediately before the planned recruitment date. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.