资源论文Using Dirichlet Free Form Deformation to Fit Deformable Models to Noisy 3-D Data

Using Dirichlet Free Form Deformation to Fit Deformable Models to Noisy 3-D Data

2020-03-23 | |  40 |   28 |   0

Abstract

Free-form deformations (FFD) constitute an important geometric shape modification method that has been extensively investigated for computer animation and geometric modelling. In this work, we show that FFDs are also very effective to fit deformable models to the kind of noisy 3–D data that vision algorithms such as stereo tend to produce. We advocate the use of Dirichlet Free Form Deformation (DFFD) instead of more conventional FFDs because they give us the ability to place control points at arbitrary locations rather than on a regular lattice, and thus much greater fiexibility. We tested our approach on stereo data acquired from monocular video-sequences and show that it can be successfully used to reconstruct a complex object such as the whole head, including the neck and the ears, as opposed to the face only.

上一篇:A Probabilistic Framework for Spatio-Temporal Video Representation & Indexing

下一篇:Robust Active Shape Model Search

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...