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In this paper we present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture.

Intrinsic surface properties are those properties which are not affected by the choice of the coordinate system, the position of the viewer relative to the surface, and the particular parameterization of the surface. In [2], Besl and Jain have argued the importance of the surface curvatures as such intrinsic properties for describing the surface. But such intrinsic properties may be useful only when they can be stably computed. Most of the techniques proposed so far for computing surface curvatures can only be applied to range data represented in image form (see [5] and references therein). But in practice, it is not always possible to represent the sampled data under this form, as in the case of closed surfaces. So other representations must be used.Surface triangulation refers to a computational structure imposed on the set of 3D points sampled from a surface to make explicit the proximity relationships between these points [1]. Such structure has been used to solve many problems [1]. One question concerning such structure is what properties of the underlying surface can be computed from it. It is obvious that some geometric properties, such as area, volume, axes of inertia, surface normals at the vertices, can be easily estimated [1]. But it is less clear how to compute some other intrinsic surface properties. In [8], a method for computing the minimal (geodesic) distance on a triangulated surface has been proposed. Lin and Perry [6] have discussed the use of surface triangulation to compute the Gaussian curvature and the genus of surface. In this paper, we propose a scheme for computing the principal curvatures at the vertices of a triangulated surface.

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Abstract-We present a new approach to camera calibration as a part of a complete and practical system to recover digital copies of sculpture from uncalibrated image sequences taken under turntable motion. In this paper, we introduce the concept of the silhouette coherence of a set of silhouettes generated by a 3D object. We show how the maximization of the silhouette coherence can be exploited to recover the camera poses and focal length. Silhouette coherence can be considered as a generalization of the well-known epipolar tangency constraint for calculating motion from silhouettes or outlines alone. Further, silhouette coherence exploits all the geometric information encoded in the silhouette (not just at epipolar tangency points) and can be used in many practical situations where point correspondences or outer epipolar tangents are unavailable. We present an algorithm for exploiting silhouette coherence to efficiently and reliably estimate camera motion. We use this algorithm to reconstruct very high quality 3D models from uncalibrated circular motion sequences, even when epipolar tangency points are not available or the silhouettes are truncated. The algorithm has been integrated into a practical system and has been tested on more than 50 uncalibrated sequences to produce high quality photo-realistic models. Three illustrative examples are included in this paper. The algorithm is also evaluated quantitatively by comparing it to a state-of-the-art system that exploits only epipolar tangents.

Abstract-We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

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