We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene\/object from a sequence of images with unknown motions. Instead of representing scene\/object by a collection of isolated 3D features (usually points), our algorithm uses a surface controlled by a small set of parameters. Compared with previous modelbased approaches, our approach has the following advantages. First, instead of using the model space as a regularizer, we directly use it as our search space, thus resulting in a more elegant formulation with fewer unknowns and fewer equations. Second, our algorithm automatically associates tracked points with their correct locations on the surfaces, thereby eliminating the need for a prior 2D-to-3D association. Third, regarding face modeling, we use a very small set of face metrics (meaningful deformations) to parameterize the face geometry, resulting in a smaller search space and a better posed system. Experiments with both synthetic and real data show that this new algorithm is faster, more accurate and more stable than existing ones.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene\/object from a sequence of images with unknown motions. Instead of representing scene\/object by a collection of isolated 3D features (usually points), our algorithm uses a surface controlled by a small set of parameters. 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