Automatic 3D facial model and texture reconstruction from range scans

This paper presents a fully automatic approach to fitting a generic facial model to detailed range scans of human faces to reconstruct 3D facial models and textures with no manual intervention (such as specifying landmarks). A Scaling Iterative Closest Points (SICP) algorithm is introduced to compute the optimal rigid registrations between the generic model and the range scans with different sizes. And then a new template-fitting method, formulated in an optmization framework of minimizing the physically based elastic energy derived from thin shells, faithfully reconstructs the surfaces and the textures from the range scans and yields dense point correspondences across the reconstructed facial models. Finally, we demonstrate a facial expression transfer method to clone facial expressions from the generic model onto the reconstructed facial models by using the deformation transfer technique

Geometric deep learning

The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas

Quantitative coronary CT angiography: absolute lumen sizing rather than %stenosis predicts hemodynamically relevant stenosis

This full day tutorial will use lectures and demonstrations from leading researchers and museum practitioners to present the principles and practices for robust photography-based digital techniques in museum contexts. The tutorial will present many examples of existing and cutting-edge uses of photography-based imaging including Reflectance Transformation Imaging (RTI), Algorithmic Rendering (AR), camera calibration, and methods of imaged-based generation of textured 3D geometry. The tutorial will also explore a framework for Leading museums are now adopting the more mature members of this family of robust digital imaging practices. These practices are part of the emerging science known as Computational Photography (CP). The imaging family’s common feature is the purpose-driven selective extraction of information from sequences of standard digital photographs. The information is extracted from the photographic sequences by computer algorithms. The extracted information is then integrated into a new digital representations containing knowledge not present in the original photogs, examined either alone or sequentially. The tutorial will examine strategies that promote widespread museum adoption of empirical acquisition technologies, generate scientifically reliable digital representations that are ‘born archival’, assist this knowledge’s long-term digital preservation, enable its future reuse for novel purposes, aid the physical conservation of the digitally represented museum materials, and enable public access and research

A performance evaluation method to compare the multi-view point cloud data registration based on ICP algorithm and reference marker

Registration of range images of surfaces is a fundamental problem in three-dimensional modelling. This process is performed by finding a rotation matrix and translation vector between two sets of data points requiring registration. Many techniques have been developed to solve the registration problem. Therefore, it is important to understand the accuracy of various registration techniques when we decide which technique will be selected to perform registration task. This paper presents a new approach to test and compare registration techniques in terms of accuracy. Among various registration methods, iterative closest point-based algorithms and reference marker methods are two types of commonly applied methods which are used to accomplish this task because they are easy to implement and relatively low cost. These two methods have been selected to perform a comprehensively quantitative evaluation by using the proposed method and the registration results are verified using the calibrated NPL freeform standard

3D modeling and motion parallax for improved videoconferencing

We consider a face-to-face videoconferencing system that uses a Kinect camera at each end of the link for 3D modeling and an ordinary 2D display for output. The Kinect camera allows a 3D model of each participant to be transmitted; the (assumed static) background is sent separately. Furthermore, the Kinect tracks the receiver’s head, allowing our system to render a view of the sender depending on the receiver’s viewpoint. The resulting motion parallax gives the receivers a strong impression of 3D viewing as they move, yet the system only needs an ordinary 2D display. This is cheaper than a full 3D system, and avoids disadvantages such as the need to wear shutter glasses, VR headsets, or to sit in a particular position required by an autostereo display. Perceptual studies show that users experience a greater sensation of depth with our system compared to a typical 2D videoconferencing system

Basic Atomic Physics

Contains reports on five research projects.Joint Services Electronics Program Contract DAAL03-92-C-0001Joint Services Electronics Program Grant DAAH04-95-1-0038National Science Foundation Grant PHY 92-21489U.S. Navy - Office of Naval Research Grant N00014-90-J-1322National Science Foundation Grant PHY 92-22768U.S. Army - Office of Scientific Research Grant DAAL03-92-G-0229U.S. Army - Office of Scientific Research Grant DAAL01-92-6-0197U.S. Navy - Office of Naval Research Grant N00014-89-J-1207Alfred P. Sloan FoundationU.S. Navy - Office of Naval Research Grant N00014-90-J-1642U.S. Navy - Office of Naval Research Grant N00014-94-1-080