The seminal multiple-view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis (MVS) methodology. The somewhat small size and variability of these data sets, however, limit their scope and the conclusions that can be derived from them. To facilitate further development within MVS, we here present a new and varied data set consisting of 80 scenes, seen from 49 or 64 accurate camera positions. This is accompanied by accurate structured light scans for reference and evaluation. In addition all images are taken under seven di↵erent lighting conditions. As a benchmark and to validate the use of our data set for obtaining reasonable and statistically significant findings about MVS, we have applied the three state-of-the-art MVS algorithms by Campbell et al., Furukawa et al., and Tola et al. to the data set. To do this we have extended the evaluation protocol from the Middlebury evaluation, necessitated by the more complex geometry of some of our scenes. The data set and accompanying evaluation framework are made freely available online.Based on this evaluation, we are able to observe several characteristics of state-of-the-art MVS, e.g. that there is a tradeo↵ between the quality of the reconstructed 3D points (accuracy) and how much of an object's surface is captured (completeness). Also, several H. Aanaes, R.R. Jensen and A.B. Dahl Technical University of Denmark, Lyngby, Denmark E-mail: {aanes, raje, abda}@dtu.dk G. Vogiatzis Aston University, Birmingham, England E-mail: g.vogiatzis@aston.ac.uk E. Tola Aurvis, Ankara, Turkey E-mail: tola@aurvis.com issues that we hypothesized would challenge MVS, such as specularities and changing lighting conditions did not pose serious problems. Our study finds that the two most pressing issues for MVS are those of meshing (forming 3D points into closed triangulated surfaces) and lack of texture.

The seminal multiple view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis methodology. Although seminal, these benchmark datasets are limited in scope with few reference scenes. Here, we try to take these works a step further by proposing a new multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, we propose a dataset containing 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot. To apply this dataset we propose an extension of the evaluation protocol from the Middlebury evaluation, reflecting the more complex geometry of some of our scenes. The proposed dataset is used to evaluate the state of the art multiview stereo algorithms of Tola et al., Campbell et al. and Furukawa et al. Hereby we demonstrate the usability of the dataset as well as gain insight into the workings and challenges of multi-view stereopsis. Through these experiments we empirically validate some of the central hypotheses of multi-view stereopsis, as well as determining and reaffirming some of the central challenges.

The nitrogen-vacancy center in diamond has been explored extensively as a light-matter interface for quantum information applications, however, it is limited by low coherent photon emission and spectral instability. Here, we present a promising interface based on an alternative defect with superior optical properties (the germanium-vacancy) coupled to a finesse-11 000 fiber cavity, resulting in a 31 +11 −15-fold increase in the spectral density of zero-phonon-line emission. This work sets the stage for cryogenic experiments, where we predict a measurable increase in the spontaneous emission rate.

A modified post-fixation procedure increases the contrast of glycogen particles in tissue. This paper provides a step-by-step protocol describing how to handle the tissue, conduct the imaging, and use stereological methods to obtain unbiased and quantitative data on fiber type-specific subcellular glycogen distribution in skeletal muscle.

An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction.