Photogrammetry Studios are a common setup to acquire high-quality 3D geometry from different kinds of real-world objects, humans, etc. In a photo studio like setup, 50 -200 DSLR cameras are used with object-specific illumination to simultaneously capture images that are processed by algorithms that automatically estimate the camera parameters and detailed geometry. These steps are automated in established pipelines to a large extent and do not require much user input. However, the post-processing typically involves a manual estimation of surface reflectance parameters by an artist, who paints textures to allow for photorealistic rendering. While professional light stages facilitate this process in an automated way, these setups are very expensive and require accurately calibrated light sources and cameras. In our work, we present a new formulation along with a practical solution to reduce these constraints to photo studio like setups by jointly reconstructing the geometric configuration of the lights along with spatially varying surface reflectance properties and its diffuse albedo. In the presented synthetic as well as real-world experiments, we analyze the effect of different optimization objectives and show that our method is able to provide photorealistic reconstruction results with an RMSE of ≈ 1 - 3% on real data.

We present a novel approach to mesh deformation that enables simple context sensitive manipulation of 3D geometry. The method is based on locally anisotropic transformations and is extended to global control directions. This allows intuitive directional modeling within an easy to implement framework. The proposed method complements current sculpting paradigms by providing further possibilities for intuitive surface-based editing without the need for additional host geometries. We show the anisotropic deformation to be seamlessly transferable to free boundary parameterization methods, which allows us to solve the hard problem of flattening compression garments in the domain of apparel design.

Hardware tessellation is one of the latest GPU features. Triangle or quad meshes are tessellated on-the-fly, where the tessellation level is chosen adaptively in a separate shader. The hardware tessellator only generates topology; attributes such as positions or texture coordinates of the newly generated vertices are determined in a domain shader. Typical applications of hardware tessellation are view dependent tessellation of parametric surfaces and displacement mapping. Often, the attributes for the newly generated vertices are stored in textures, which requires uv unwrapping, chartification, and atlas generation of the input mesh--a process that is time consuming and often requires manual intervention. In this paper, we present an alternative representation that directly stores optimized attribute values for typical hardware tessellation patterns and simply assigns these attributes to the generated vertices at render time. Using a multilevel fitting approach, the attribute values are optimized for several resolutions. Thereby, we require no parameterization, save memory by adapting the density of the samples to the content, and avoid discontinuities by construction. Our representation is optimally suited for displacement mapping: it automatically generates seamless, view-dependent displacement mapped models. The multilevel fitting approach generates better low-resolution displacement maps than simple downfiltering. By properly blending levels, we avoid artifacts such as popping or swimming surfaces. We also show other possible applications such as signal-optimized texturing or light baking. Our representation can be evaluated in a pixel shader, resulting in signal adaptive, parameterization-free texturing, comparable to PTex or Mesh Colors. Performance evaluation shows that our representation is on par with standard texture mapping and can be updated in real time, allowing for application such as interactive sculpting.

We 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.

Abstract 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. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract 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.

Abstract 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.

Abstract We present a novel method to animate a static geometry by cloning a captured animation sequence. More precisely, given a sequence of range scans of a deforming object which has been captured by a real‐time 3D scanner, we describe a novel algorithm to clone the animation of the recorded geometry onto another triangle mesh. To achieve this, we reconstruct a coherent animated mesh of the input sequence using a template deformation approach. Then we employ a new algorithm for robust marker‐less non‐rigid registration to deform one frame of the generated animation such that it matches a different 3D model. The resulting registration is further used to find correspondences between the animation and the target object which are in turn used to transfer the animation of the recorded sequence onto the target shape. Transferring the entire geometry of the animations results in very convincing facial expressions since even the smallest expression wrinkles are preserved. We evaluate the robustness and the performance of the proposed algorithms using a variety of data sets, including facial animations and whole body animations. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract In this paper, we describe a novel approach for the reconstruction of animated meshes from a series of time‐deforming 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 ℝ4 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.