As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this paper, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D

Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is a simpler problem than the comparison of 3D surfaces by traditional shape matching methods that require pose registration, feature correspondence, or model fitting. We find that the dissimilarities be...

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 decomposed. In recent years, in computer graphics, polygonal meshes are decomposed into sub-meshes. In this paper we propose a novel hierarchical mesh decomposition algorithm. Our algorithm computes a decomposition into the meaningful components of a given mesh, which generally refers to segmentation at regions of deep concavities. The algorithm also avoids over-segmentation and jaggy boundaries between the components. Finally, we demonstrate the utility of the algorithm in control-skeleton extraction.

Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

In this paper, we describe a novel method for searching and comparing 3D objects. The method encodes the geometric and topological information in the form of a skeletal graph and uses graph matching techniques to match the skeletons and to compare them. The skeletal graphs can be manually annotated to refine or restructure the search. This helps in choosing between a topological similarity and a geometric (shape) similarity. A feature of skeletal matching is the ability to perform part-matching, and its inherent intuitiveness, which helps in defining the search and in visualizing the results. Also, the matching results, which are presented in a per-node basis can be used for driving a number of registration algorithms, most of which require a good initial guess to perform registration. In this paper, we also describe a visualization tool to aid in the selection and specification of the matched objects.

In this paper, we propose a new scheme for free-form skeleton-driven global mesh deformations. First a Voronoi-based skeletal mesh is extracted from a given original mesh. Next the skeletal mesh is modified by free-form deformations. Then a desired global shape deformation is obtained by reconstructing the shape corresponding to the deformed skeletal mesh. We develop a mesh fairing procedure allowing us to avoid possible global and local self-intersections of the reconstructed mesh. Finally, using DSS [16] shape representation improves the speed and robustness of our approach.

We present an approach to real-time animation of deformable objects. Optimization of algorithms using compactly supported radial basis functions (CSRBF) allows us to generate deformations performed fast enough for such real-time applications as computer games. The algorithm described in detail in this paper uses space mapping technique. Smooth local deformations of animation objects can be defined by only a moderate number of control vectors and locality of deformations can be defined by radius of support. We also present examples of animations and speed benchmarks.

Current approaches to skeleton generation are based on topological and geometrical information only; this can be insufficient for realistic character animation, since the location of the joints does not usually match the real bone structure of the model. This paper proposes the use of anatomical information to enhance the skeleton. Using a harmonic function, this information can be recovered from the skeleton itself, which is guaranteed not to have undesired endpoints. The skeleton is computed as a Reeb graph of such a function over the surface of the model. Starting from one point selected on the head of the character, the entire process is fast, automatic and robust; it generates skeletons whose joints can be associated with the character’s anatomy. Results are provided, including a quantitative validation of the generated skeletons. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism: Animation 1.

Abstract.This paper presents a Bayesian multi-scale three dimensional deformable template approach based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery. Prior information about the geometry and shape of the anatomical objects under study is incorporated via the construction of exemplary templates. The anatomical variability is accommodated in the Bayesian framework by defining probabilistic transformations on these templates. The modeling approach taken in this paper for building exemplary templates and associated transformations is based on a multi-scale medial representation. The transformations defined in this framework are parameterized directly in terms of natural shape operations, such as thickening and bending, and their location. Quantitative validation results are presented on the automatic segmentation procedure developed for the extraction of the kidney parenchyma-including the renal pelvis-in subjects undergoing radiation treatment for cancer. We show that the segmentation procedure developed in this paper is efficient and accurate to within the voxel resolution of the imaging modality. A

We address the problem of representing 3D shapes when partial and unorganized data is obtained as an input, such as clouds of point samples on the surface of a face, statue, solid, etc., of regular or arbitrary complexity (free-form), as is commonly produced by photogrammetry, laser scanners, computerized tomography, and so on. Our starting point is the medial axis (MA) representation which has been explored mainly for 2D problems since the 1960's in pattern recognition and image analysis. The MA makes explicit certain symmetries of an object, corresponding to the shocks of waves initiated at the input samples, but is itself difficult to directly use for recognition tasks and applications. Based on previous work on the 2D problem, we propose a new representation in 3D which is derived from the MA, producing a graph we call the shock scaffold. The nodes of this graph are defined to be certain singularities of the shock flow along the MA. This graph can represent exactly the MA --- and the original inputs --- or approximate it, leading to a hierarchical description of shapes. We develop accurate and efficient algorithms to compute for 3D unorganized clouds of points the shock scaffold, and thus the MA, as well as its close cousin the Voronoi diagram. One computational method relies on clustering and visibility constraints, while the other simulates wavefront propagation on a 3D grid. We then propose a method of splitting the shock scaffold in two sub-graphs, one of which is related to the (a priori unknown) surface of the object under scrutiny. This allows us to simplify the shock scaffold making more explicit coarse scale object symmetries, while at the same time providing an original method for the surface interpolation of complex datasets. In the last part of this talk, we address extensions of the shock scaffold by studying the case where the inputs are given as collections of unorganized polygons. Keywords: 3D shape representation, medial axis, Voronoi diagram, maximal contact spheres and shocks, directed graphs (digraphs), shock scaffold hierarchy (5 levels), wave propagation, eikonal equation, Euclidean distance maps, Lagrangian versus Eulerian computations, deterministic celullar automata, Huygens versus Fermat's optical principles, visibility constraints, unorganized generators, point clouds, polygonal clouds, quadrics, quartics, octics, Groebner bases and hybrid elimination methods, surface interpolation and meshing, ribs and ridges.