This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes. An objective function is formulated as a Conditional Random Field model, with terms assessing the consistency of faces with labels, and terms between labels of neighboring faces. The objective function is learned from a collection of labeled training meshes. The algorithm uses hundreds of geometric and contextual label features and learns different types of segmentations for different tasks, without requiring manual parameter tuning. Our algorithm achieves a significant improvement in results over the state-of-the-art when evaluated on the Princeton Segmentation Benchmark, often producing segmentations and labelings comparable to those produced by humans.

/pdf/learning-3d-mesh-segmentation-and-labeling-59vi9rtfjg.pdf

Learning 3D mesh segmentation and labeling

Mesh partitioning and skeletonisation are fundamental for many computer graphics and animation techniques. Because of the close link between an object’s skeleton and its boundary, these two problems are in many cases complementary. Any partitioning of the object can assist in the creation of a skeleton and any segmentation of the skeleton can infer a partitioning of the object. In this paper, we consider these two problems on a wide variety of meshes, and strive to construct partitioning and skeletons which remain consistent across a family of objects, not a single one. Such families can consist of either a single object in multiple poses and resolutions, or multiple objects which have a general common shape. To achieve consistency, we base our algorithms on a volume-based shape-function called the shape-diameter-function (SDF), which remains largely oblivious to pose changes of the same object and maintains similar values in analogue parts of different objects. The SDF is a scalar function defined on the mesh surface; however, it expresses a measure of the diameter of the object’s volume in the neighborhood of each point on the surface. Using the SDF we are able to process and manipulate families of objects which contain similarities using a simple and consistent algorithm: consistently partitioning and creating skeletons among multiple meshes.

Consistent mesh partitioning and skeletonisation using the shape diameter function

https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/135706/1/yigak03086.pdf

Pore throat size and connectivity determine bone and tissue ingrowth into porous implants : Three-dimensional micro-CT based structural analyses of porous bioactive titanium implants

Cutting up a complex object into simpler sub-objects is a fundamental problem in various disciplines. In image processing, images are segmented while in computational geometry, solid polyhedra are ...

Hierarchical mesh decomposition using fuzzy clustering and cuts

Mesh segmentation has be- come a necessary ingredient in many applications in computer graphics. This paper proposes a novel hierarch- ical mesh segmentation algorithm, which is based on new methods for prominent feature point and core extraction. The algorithm has several benefits. First, it is invariant both to the pose of the model and to different proportions between the model's components. Second, it produces correct hierarchical segmentations of meshes, both in the coarse levels of the hierarchy and in the fine levels, where tiny segments are extracted. Finally, the boundaries between the segments go along the natural seams of the models.

/pdf/mesh-segmentation-using-feature-point-and-core-extraction-51dtbhq0zi.pdf

Mesh segmentation using feature point and core extraction

This paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design.

Partitioning 3D surface meshes using watershed segmentation

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Partitioning 3D surface meshes using watershed segmentation