Curvature-aware adaptive re-sampling for point-sampled geometry Export

@article{citeulike:4010909, abstract = {With the emergence of large-scale point-sampled geometry acquired by high-resolution 3D scanning devices, it has become increasingly important to develop efficient algorithms for processing such models which have abundant geometric details and complex topology in general. As a preprocessing step, surface simplification is important and necessary for the subsequent operations and geometric processing. Owing to adaptive mean-shift clustering scheme, a curvature-aware adaptive re-sampling method is proposed for point-sampled geometry simplification. The generated sampling points are non-uniformly distributed and can account for the local geometric feature in a curvature aware manner, i.e., in the simplified model, the sampling points are dense in the high curvature regions, and sparse in the low curvature regions. The proposed method has been implemented and demonstrated on several examples.}, author = {Miao, Y. and Pajarola, R. and Feng, J.}, citeulike-article-id = {4010909}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.cad.2009.01.006}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0010448509000220}, doi = {10.1016/j.cad.2009.01.006}, issn = {00104485}, journal = {Computer-Aided Design}, keywords = {sampling}, month = {January}, posted-at = {2009-02-05 11:00:12}, priority = {2}, title = {Curvature-aware adaptive re-sampling for point-sampled geometry}, url = {http://dx.doi.org/10.1016/j.cad.2009.01.006}, year = {2009} }

TY - JOUR ID - citeulike:4010909 L3 - citeulike-article-id:4010909 N2 - With the emergence of large-scale point-sampled geometry acquired by high-resolution 3D scanning devices, it has become increasingly important to develop efficient algorithms for processing such models which have abundant geometric details and complex topology in general. As a preprocessing step, surface simplification is important and necessary for the subsequent operations and geometric processing. Owing to adaptive mean-shift clustering scheme, a curvature-aware adaptive re-sampling method is proposed for point-sampled geometry simplification. The generated sampling points are non-uniformly distributed and can account for the local geometric feature in a curvature aware manner, i.e., in the simplified model, the sampling points are dense in the high curvature regions, and sparse in the low curvature regions. The proposed method has been implemented and demonstrated on several examples. JF - Computer-Aided Design SN - 00104485 TI - Curvature-aware adaptive re-sampling for point-sampled geometry KW - sampling AU - Miao, Y AU - Pajarola, R AU - Feng, J PY - 2009/01/30/ UR - http://dx.doi.org/10.1016/j.cad.2009.01.006 ER -