Layout Consistent Segmentation of 3-D Meshes via Conditional Random Fields and Spatial Ordering Constraints

摘要：

We address the problem of 3-D Mesh segmentation for categories of objects with known part structure. Part labels are derived from a semantic interpretation of non-overlapping subsurfaces. Our approach models the label distribution using a Conditional Random Field (CRF) that imposes constraints on the relative spatial arrangement of neighboring labels, thereby ensuring semantic consistency. To this end, each label variable is associated with a rich shape descriptor that is intrinsic to the surface. Randomized decision trees and cross validation are employed for learning the model, which is eventually applied using graph cuts. The method is flexible enough for segmenting even geometrically less structured regions and is robust to local and global shape variations.

作者: Alexander Zouhar Sajjad Baloch Yanghai Tsin Tong Fang Siegfried Fuchs

作者单位: Siemens Corporate Research, Inc., Princeton, USA Dresden University of Technology, Dresden, Germany Siemens Corporate Research, Inc., Princeton, USA Dresden University of Technology, Dresden, Germany

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 113–120

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）