Abstract
CURRENT techniques for monocular reconstruction either require dense correspondences and rely on motion and deformation cues, or assume a highly accurate reconstruction (referred to as a template) of at least a single frame given in advance and operate in the manner of non-rigid tracking. Accurate computation of dense point tracks often requires multiple frames and can be computationally expensive. Availability of a template is a very strong prior which restricts system operation to a pre-defined environment and scenarios (see chapter 3.1 for the detailed introduction to the field and the state of the art).
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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature
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Golyanik, V. (2020). Monocular Non-Rigid Surface Reconstruction with Learned Deformation Model. In: Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-30567-3_7
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DOI: https://doi.org/10.1007/978-3-658-30567-3_7
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