Improved Part-Based Segmentation of Voxel Shapes by Skeleton Cut Spaces

Abstract

Abstract:We present a re ned method for part-based segmentation of voxel shapes by constructing partitioning cuts from every voxel of the shape's medial surface. Our cuts have several desirable propertiessmoothness, tightness, and orientation with respect to the shape's local symmetry axis, making it a good segmentation tool. We analyze the space of all cuts created for a given shape and detect cuts which are good segment borders. We present a detailed analysis of the parameter space of our method, which yields good preset values for all its parameters. Our method is robust to noise, pose invariant, independent on the shape geometry and genus, and is simple to implement. We demonstrate our method for both automatic and interactive segmentation on a wide selection of 3D shapes.

Scalable Visual Exploration of 3D Shape Databases via Feature Synthesis and Selection

“…Local descriptors [24,27,31,34] capture more fine-grained, local, aspects of the shape, and are used to refine the search process. Topological descriptors, such as using 3D curve skeletons [11] or 3D surface skeletons [8] capture the part-whole structure of a shape, and are particularly good for queries which require pose invariance. Finally, view-based descriptors [5,28] describe shapes based on views thereof taken from multiple viewpoints.…”

Co-skeletons: Consistent curve skeletons for shape families

“…Lien et al [68] formulate (and solve) shape segmentation and curve-skeleton computation as a joint optimization problem. Feng et al [69] extend the geodesic-cut-based segmentation in [65,66] to surface skeletons, which encode both shape geometry and topology, thus provide more information for the segmentation. Finally, Li et al [21] use mesh decimation methods for both shape segmentation but also their curve-skeleton computation.…”

Analysis and Exploration of Large 3D Shape Databases

“…Examples of such histogram descriptors that we use in our work are shape contexts [8] and fast point feature histograms (FPFH) [129]. We note that, besides surface local features de ned on Ω, also features extracted from the shape's 3D curve skeleton, such as the local diameter of skeletal cuts, can be used to create such global descriptors via histograms [46,47]. We use histogram-based descriptors further in our work on shape database exploration (Chapter 3) and also for shape analysis (Chapter 6).…”

“…Local descriptors, such as saliency, shape thickness, and shape contexts capture more ne-grained shape details [129,131,138,151]. Topological descriptors, such as based on curve skeletons [69] or surface skeletons [47] capture the part-whole shape structure. Finally, view-based descriptors capture the appearance of the shape from multiple viewpoints [32,133].…”