Abstract. We consider 3D object retrieval in which a polygonal mesh serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step in which models are transformed into canonical coordinates. Second, feature vectors are extracted and compared withthose derived from normalized models in the search space. In the feature vector space nearest neighbors are computed and ranked. Retrieved objects are displayed for inspection, selection, and processing. Our feature vectors are based on rays cast from the center of mass of the object. For each ray the object extent in the ray direction yields a sample of a function on the sphere. We compared two kinds of representations of this function, namely spherical harmonics and moments. Our empirical comparison using precision-recall diagrams for retrieval results in a data base of 3D models showed that the method using spherical harmonics performed better. 1

Content based 3D shape retrieval for broad domains like the World Wide Web has recently gained considerable attention in Computer Graphics community. One of the main challenges in this context is the mapping of 3D objects into compact canonical representations referred to as descriptors, which serve as search keys during the retrieval process. The descriptors should have certain desirable properties like invariance under scaling, rotation and translation. Very importantly, they should possess descriptive power providing a basis for similarity measure between three-dimensional objects which is close to the human notion of resemblance. In this paper we advocate the usage of so-called 3D Zernike invariants as descriptors for content based 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under the above transformations. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like type and resolution of the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of spherical harmonics based descriptors, which are reported to be among the most successful representations at present. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance.

Along with the development of 3D acquisition devices and methods and increasing number of available 3D objects, new tools are necessary to automatically analyze, search and interpret these models. In this paper we describe a novel geometric approach to 3D object comparison and analysis. To compare two objects geometrically we first properly position and align the objects. After solving this pose estimation problem, we generate specific distance histograms that define a measure of geometric similarity of the inspected objects.

We advocate the usage of 3D Zernike invariants as descriptors for 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under rotation, translation and scaling. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of recently introduced spherical harmonics based descriptors. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance using several quality measures and based on experiments on the Princeton Shape Benchmark. 1

In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invariance is commonly used in conceptionally different ways. We propose a more distinguishing treatment in particular in the active field of kernel methods for machine learning and pattern analysis. Additionally, the fundamental relation of invariant kernels and traditional invariant pattern analysis by means of invariant representations will be clarified. After addressing these conceptional questions, we focus on practical aspects and present two generic approaches for constructing invariant kernels. The first approach is based on a technique called invariant integration. The second approach builds on invariant distances. In principle, our approaches support general transformations in particular covering discrete and non-group or even an infinite number of pattern-transformations. Additionally, both enable a smooth interpolation between invariant and non-invariant pattern analysis, i.e. they are a covering general framework. The wide applicability and various possible benefits of invariant kernels are demonstrated in different kernel methods.

Abstract. This paper addresses the problem of reconstructing textureless objects of quadric like shape. It is known that a quadric can be uniquely recovered from its apparent contours in three views. But, in the case of only two views the reconstruction is a one parameter family of quadrics. Polarization imaging provides additional geometric information compared to simple intensity based imaging. The polarization image encodes the projection of the surface normals onto the image and therefore provides constraints on the surface geometry. In this paper it is proven that two polarization views of a quadric contain sufficient information for a complete determination of its shape. The proof itself is constructive leading to a closed-form solution for the quadric. Additionally, an indirect algorithm is presented which uses both polarization and apparent contours. By experiments it is shown that the presented algorithm produces accurate reconstruction results. 1

In this paper we propose to extend the well known spherical harmonic descriptors[6] (SHD) by adding an additional Fourier-like radial expansion to represent volumetric data. Having created an orthonormal basis on the ball with all the gentle properties known from the spherical harmonics theory and Fourier theory, we are able to compute efficiently a multi-scale representation of 3D objects that leads to highly discriminative rotation-invariant features, which will be called spherical Fourier harmonic descriptors (SFHD). Experiments on the challenging Princeton Shape Benchmark (PSB[16]) demonstrate the superiority of SFHD over the ordinary SHD. 1.