The development of a free stereopsis test for active shutter displays

While many people enjoy S-3D, it is a well-known fact that a minority of the population is not able to perceive S-3D. These people have problems with stereopsis, or the ability of our brain to unconsciously fuse two 2D images into a single 3D percept. In clinical practice, several stereopsis tests are used to measure this deficiency. Most of these tests are expensive paper-and-pencil tests requiring trained observers. In this paper, we discuss a recently developed method to test stereopsis on active shutter glasses displays. This allows researchers in the lab or S-3D users at home to test stereopsis in a free and easy way. Furthermore, we were interested in the distribution of test scores. More specifically, we wanted to know if our test resulted in a continuous (graded stereopsis) or bipolar (stereopsis present or not) distribution. Results of a preliminary study (N = 128) showed evidence for the second

Testing for unit roots in three-dimensional heterogeneous panels in the presence of cross-sectional dependence

This paper extends the cross-sectionally augmented IPS (CIPS) test of Pesaran (2006) to a three-dimensional (3D) panel. This 3D-CIPS test is correctly sized in the presence of cross-sectional dependency. Comparing its power performance to that of a bootstrapped IPS (BIPS) test, we find that the BIPS test invariably dominates, although for high levels of cross-sectional dependency the 3D-CIPS test can out-perform the BIPS test.Heterogeneous dynamic panels ; Monte Carlo ; unit roots ; cross-sectional dependence

Test beam Characterizations of 3D Silicon Pixel detectors

3D silicon detectors are characterized by cylindrical electrodes perpendicular to the surface and penetrating into the bulk material in contrast to standard Si detectors with planar electrodes on its top and bottom. This geometry renders them particularly interesting to be used in environments where standard silicon detectors have limitations, such as for example the radiation environment expected in an LHC upgrade. For the first time, several 3D sensors were assembled as hybrid pixel detectors using the ATLAS-pixel front-end chip and readout electronics. Devices with different electrode configurations have been characterized in a 100 GeV pion beam at the CERN SPS. Here we report results on unirradiated devices with three 3D electrodes per 50 x 400 um2 pixel area. Full charge collection is obtained already with comparatively low bias voltages around 10 V. Spatial resolution with binary readout is obtained as expected from the cell dimensions. Efficiencies of 95.9% +- 0.1 % for tracks parallel to the electrodes and of 99.9% +- 0.1 % at 15 degrees are measured. The homogeneity of the efficiency over the pixel area and charge sharing are characterized.Comment: 5 pages, 7 figure

Test of Universality in Anisotropic 3D Ising Model

Chen and Dohm predicted theoretically in 2004 that the widely believed universality principle is violated in the Ising model on the simple cubic lattice with more than only six nearest neighbours. Schulte and Drope by Monte Carlo simulations found such violation, but not in the predicted direction. Selke and Shchur tested the square lattice. Here we check only this universality for the susceptibility ratio near the critical point. For this purpose we study first the standard Ising model on a simple cubic lattice with six nearest neighbours, then with six nearest and twelve next-nearest neighbours, and compare the results with the Chen-Dohm lattice of six nearest neighbours and only half of the twelve next-nearest neighbours. We do not confirm the violation of universality found by Schulte and Drope in the susceptibility ratio.Comment: 6 pages including 4 figures, Physica A, in pres

A 3D radiative transfer framework IX. Time dependence

Context. Time-dependent, 3D radiation transfer calculations are important for the modeling of a variety of objects, from supernovae and novae to simulations of stellar variability and activity. Furthermore, time-dependent calculations can be used to obtain a 3D radiative equilibrium model structure via relaxation in time. Aims. We extend our 3D radiative transfer framework to include direct time dependence of the radiation field; i.e., the $\partial I/ \partial t$ terms are fully considered in the solution of radiative transfer problems. Methods. We build on the framework that we have described in previous papers in this series and develop a subvoxel method for the $\partial I/\partial t$ terms. Results. We test the implementation by comparing the 3D results to our well tested 1D time dependent radiative transfer code in spherical symmetry. A simple 3D test model is also presented. Conclusions. The 3D time dependent radiative transfer method is now included in our 3D RT framework and in PHOENIX/3D.Comment: A&A in press, 7 pages, 14 figure

A Blow-Up Criterion for the 3D Euler Equations Via the Euler-Voigt Inviscid Regularization

We propose a new blow-up criterion for the 3D Euler equations of incompressible fluid flows, based on the 3D Euler-Voigt inviscid regularization. This criterion is similar in character to a criterion proposed in a previous work by the authors, but it is stronger, and better adapted for computational tests. The 3D Euler-Voigt equations enjoy global well-posedness, and moreover are more tractable to simulate than the 3D Euler equations. A major advantage of these new criteria is that one only needs to simulate the 3D Euler-Voigt, and not the 3D Euler equations, to test the blow-up criteria, for the 3D Euler equations, computationally

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page