Robust Shape Collection Matching and Correspondence from Shape Differences

Similar to the recently presented Fast Lattice Shape Matching (FLSM), we compute the position of simulation nodes by convolution of rigid shape matching operators on many

Experimenting with computer vision tasks, such as image matching and optical flow, the use of the wavelet based belief propagation approach facilitates not only theoretical but also

Content-based shape retrieval using the shape itself as a query and based on the comparison of geometric and topo- logical properties of shapes is complicated by the fact that many

Given a point cloud, in the form of unorganized points, the problem of auto- matically connecting the dots to obtain an aesthetically pleasing and piecewise-linear closed

Whether cut regions are patched up or not, the geodesic information on a partial surface slightly changes with respect to its complete version, introducing some extra imperfection

It covers algorithms for establishing correspondence, methods for modeling shape variation, image segmentation algorithms such as the Active Shape Model and evaluation methodology

Due to the variations in the types and robustness considerations in retrieval per- formance, we employ the hybrid shape descriptor ZFDR devised in [LJ13] which integrates both

For shape matching, we incorporate the result of two paral- lel processes: (i) local shape similarity assessment by aver- aging the minimum weighted distances associated with pairs