Subdivision Surfaces in Computer Vision

In vision and machine learning, almost everything we do may be considered to be a form of model fitting. Whether estimating the parameters of a convolutional neural network, computing structure and motion from image collections, tracking objects in video, computing low-dimensional representations of datasets, estimating parameters for an inference model such as Markov random fields, or extracting shape spaces such as active appearance models, it almost always boils down to minimizing an objective containing some parameters of interest as well as some latent or nuisance parameters.

This project explores several tools and techniques for solving model-fitting problems, with a focus on fitting 3D smooth-surface models, such as subdivision surfaces, to 2D and 3D data.

Link: CVPR Tutorial (opens in new tab)