Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers, who pursue innovation in a range of scientific and technical disciplines to help solve complex challenges in diverse fields, including computing, healthcare, economics, and the environment. Traditional methods in geometric computer vision, which are still the go-to techniques for robot navigation, three-dimensional (3D) scene reconstruction, and the real-time generation of Augmented Reality/Virtual Reality (AR/VR) assets, are based on frequentist statistical methods, in particular maximum likelihood estimation. The goal of this Research Internship is to develop a basis for the application of Bayesian methods in geometric computer vision, starting with the fundamental problem of 2D and 3D point matching. Candidates should have research-level knowledge of two of the three following topics (and, if required, the drive to learn the third), demonstrated through publications or explicit direction of their PhD work: Geometric computer vision, including: The pinhole camera model, homographies, essential and fundamental matrix; three-dimensional point triangulation and iterative closest-point methods. Associated statistical methods such as RANSAC and maximum likelihood estimation; estimation of geometric and algebraic errors associated with geometric parameters of interest. Bayesian statistical theory, including: Jeffreys' and uniform priors; exact and approximate marginalization techniques; nuisance parameters and outliers from a Bayesian perspective. Rigorous methods in approximation theory, including: Polynomial randomized approximation algorithms; computational complexity classes; approximation methods for discrete problems. Note that heuristic and learning-based methods are outside the scope of the work. Execution of the Research Internship plan will require efficient implementation of approximation algorithms in the context of one or more problems in geometric computer vision, and the benchmarking of the results against competing methods. Therefore, ability to produce efficient code in Python or C++ is a must. The Research Internship has the explicit goal of producing a high-quality technical report, which, if the work is successful, will be submitted to an appropriate publication venue.
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Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees