Complex 3D Shape Recovery using Hybrid Geometric Shape Features in aHierarchical Shape Segmentation Approach

Abstract

We present a novel and reliable approach for complex object acquisition and surface registration using hybrid geometric shape features in a hierarchical 3D shape approximation and segmentation approach. First, instead of relying on one type of scanned data, we propose to use hybrid data provided that it can support both global and local geometric shape features. The scanned low-resolution global data supplies the global shape prior for registering the high-resolution local surface patches. Local surfaces can thus be optimally registered requiring less overlap and reducing uncertainty. Second, we cannot directly register huge volumes of data simultaneously due to the memory bottlenecks. We segment the global low-resolution model into several meaningful sub-shapes extending a hierarchical algorithm. The local surfaces can be registered on the sub-shapes respectively and all sub-shapes can be merged and rendered after registration. To verify the reliability of the approach, various 3D models have been acquired. The experiments show compelling results by reconstructing very detailed models of complex objects. The approach can be applied to practical 3D modeling applications.