The problem of digitally describing a 3D world has existed since the beginnings of computer graphics. Most approaches are based on the reconstruction of a world from a set of photographs of the same scene. The latest methods are based on deep learning, which allows for direct estimation of radiance fields. The subsequent development of these methods increases speed, accuracy and accessibility. The goal of this thesis is to review the field and to compare the chosen methods for radiance field estimation. In the experimental analysis, we evaluate the quality of the methods, their dependence on resolution, the number of input images and their computational resource requirements.
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