In this paper, we describe a novel approach for the reconstruction of animated meshes from a series of timedeforming point clouds. Given a set of unordered point clouds that have been captured by a fast 3-D scanner, our algorithm is able to compute coherent meshes which approximate the input data at arbitrary time instances. Our method is based on the computation of an implicit function in R 4 that approximates the time-space surface of the time-varying point cloud. We then use the four-dimensional implicit function to reconstruct a polygonal model for the first time-step. By sliding this template mesh along the time-space surface in an as-rigid-as-possible manner, we obtain reconstructions for further time-steps which have the same connectivity as the previously extracted mesh while recovering rigid motion exactly. The resulting animated meshes allow accurate motion tracking of arbitrary points and are well suited for animation compression. We demonstrate the qualities of the proposed method by applying it to several data sets acquired by real-time 3-D scanners.
We present a simple algorithm for computing a high-quality personalized avatar from a single color image and the corresponding depth map which have been captured by Microsoft's Kinect sensor. Due to the low market price of our hardware setup, 3D face scanning becomes feasible for home use. The proposed algorithm combines the advantages of robust non-rigid registration and fitting of a morphable face model. We obtain a high-quality reconstruction of the facial geometry and texture along with one-to-one correspondences with our generic face model. This representation allows for a wide range of further applications such as facial animation or manipulation. Our algorithm has proven to be very robust. Since it does not require any user interaction, even non-expert users can easily create their own personalized avatars.
In this paper we analyze normal vector representations. We derive the error of the most widely used representation, namely 3D floating-point normal vectors. Based on this analysis, we show that, in theory, the discretization error inherent to single precision floating-point normals can be achieved by 2 50.2 uniformly distributed normals, addressable by 51 bits. We review common sphere parameterizations and show that octahedron normal vectors perform best: they are fast and stable to compute, have a controllable error, and require only 1 bit more than the theoretical optimal discretization with the same error.
Figure 1: Reproducing fine surface detail using our method for signal optimal displacement mapping. From left to right: Augustus model and close-ups of the full resolution model (366 MB GPU memory, 19.3 ms rendering time), and our reconstructions with fitting errors εmax = 0.1 (90 MB, 11.9 ms) and εmax = 0.5 (28 MB, 3.7 ms), respectively. AbstractWe present a novel representation for storing sub-triangle signals, such as colors, normals, or displacements directly with the triangle mesh. Signal samples are stored as guided by hardware-tessellation patterns. Thus, we can directly render from our representation by assigning signal samples to attributes of vertices generated by the hardware tessellator.Contrary to texture mapping, our approach does not require any atlas generation, chartification, or uv-unwrapping. Thus, it does not suffer from texture-related artifacts, such as discontinuities across chart boundaries or distortion. Moreover, our approach allows specifying the optimal sampling rate adaptively on a per triangle basis, resulting in significant memory savings for most signal types.We propose a signal optimal approach for converting arbitrary signals, including existing assets with textures or mesh colors, into our representation. Further, we provide efficient algorithms for mipmapping, bi-and tri-linear interpolation directly in our representation. Our approach is optimally suited for displacement mapping: it automatically generates crack-free, view-dependent displacement mapped models enabling continuous level-of-detail.
In this paper we address the problem of optimal centre placement for scattered data approximation using radial basis functions (RBFs) by introducing the concept of floating centres. Given an initial least-squares solution, we optimize the positions and the weights of the RBF centres by minimizing a non-linear error function. By optimizing the centre positions, we obtain better approximations with a lower number of centres, which improves the numerical stability of the fitting procedure. We combine the non-linear RBF fitting with a hierarchical domain decomposition technique. This provides a powerful tool for surface reconstruction from oriented point samples. By directly incorporating point normal vectors into the optimization process, we avoid the use of off-surface points which results in less computational overhead and reduces undesired surface artefacts. We demonstrate that the proposed surface reconstruction technique is as robust as recent methods, which compute the indicator function of the solid described by the point samples. In contrast to indicator function based methods, our method computes a global distance field that can directly be used for shape registration.
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