11,791 research outputs found
An Efficient Approach to Correspondences between Multiple Non-Rigid Parts
Identifying multiple deformable parts on meshes and establishing dense correspondences between them are tasks
of fundamental importance to computer graphics, with applications to e.g. geometric edit propagation and texture
transfer. Much research has considered establishing correspondences between non-rigid surfaces, but little
work can both identify similar multiple deformable parts and handle partial shape correspondences. This paper
addresses two related problems, treating them as a whole: (i) identifying similar deformable parts on a mesh,
related by a non-rigid transformation to a given query part, and (ii) establishing dense point correspondences
automatically between such parts. We show that simple and efficient techniques can be developed if we make the
assumption that these parts locally undergo isometric deformation. Our insight is that similar deformable parts
are suggested by large clusters of point correspondences that are isometrically consistent. Once such parts are
identified, dense point correspondences can be obtained by an iterative propagation process. Our techniques are
applicable to models with arbitrary topology. Various examples demonstrate the effectiveness of our techniques
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
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