Range Tracing

In this report, we tackle the problem of merging an arbitrary number of range scans (depth images) into a single surface mesh. The mesh-based representation is superior to point-based approaches since it contains important connectivity information. Most previous mesh-based merge methods, however, lose surface details by using simplifying intermediate surface representations (e.g.\ implicit functions). Such details are essential for further processing steps, especially for feature-preserving reconstruction methods. Our method preserves all information (connectivity and the original measurement positions) as edges and vertices of a merged surface mesh. It avoids aliasing and smoothing artifacts, adapts to the local scanner sampling and is independent of the overlap size of the input range scans. The algorithm consists of only two basic operations and is therefore simple to implement. We evaluate the performance of our approach on highly detailed real-world scans acquired with different devices

Efficient Surface Reconstruction for Piecewise Smooth Objects

In this report we present a new surface reconstruction technique from unstructured point clouds for piecewise smooth objects, such as scans of architectural and other man-made artifacts. The new technique operates in three conceptual steps: First, a set of basis functions is computed and a topology is established among these functions that respect sharp features using a RANSAC technique. Second, a linearized, statistically motivated optimization problem is solved employing this discretization. Lastly, an implicit function based meshing technique is employed to determine a clean, manifold mesh representation. The main benefit of our new proposal in comparison to previous work is its robustness and efficiency, which we examine by applying the algorithm to a set of synthetic and real-world benchmark data sets

Nonlinear quantum logic with colliding graphene plasmons

Graphene has emerged as a promising platform to bring nonlinear quantum optics to the nanoscale, where a large intrinsic optical nonlinearity enables long-lived and actively tunable plasmon polaritons to strongly interact. Here we theoretically study the collision between two counter-propagating plasmons in a graphene nanoribbon, where transversal subwavelength confinement endows propagating plasmons with %large effective masses a flat band dispersion that enhances their interaction. This scenario presents interesting possibilities towards the implementation of multi-mode polaritonic gates that circumvent limitations imposed by the Shapiro no-go theorem for photonic gates in nonlinear optical fibers. As a paradigmatic example we demonstrate the feasibility of a high fidelity conditional $\pi$ phase shift (CZ), where the gate performance is fundamentally limited only by the single plasmon lifetime. These results open new exciting avenues towards quantum information and many-body applications with strongly-interacting polaritons.Comment: 13 pages, 4 figure

Statistical Reconstruction of Indoor Scenes

In this paper we consider the problem of processing scanned datasets of man-made scenes such as building interiors and office environments. Such datasets are produced in huge quantity and often share a simple structure with sharp crease lines. However, their usual acquisition with mobile devices often leads to poor data quality and established reconstruction methods fail – at least at reconstructing sharp features. We propose to overcome the lack of reliable information by using a strong shape prior in the reconstruction method: we assume that the scene can be represented as a collection of cuboid shapes, each covering a subset of the data. The optimal configuration of cuboids is found by formulating the reconstruction problem as a discrete maximum a posteriori (MAP) optimization in a statistical sense. We propose a greedy algorithm which iteratively extracts new shape candidates and optimizes over the shape of the cuboids. A new candidate is selected by scoring its ability to reconstruct previously uncovered data points. The iteration converges at the first significant drop in the score of new candidates. Our method is fast and extremely robust to noisy and incomplete data which we show by applying it to scanned datasets acquired with different devices

Efficient Reconstruction of Non-rigid Shape and Motion from Real-Time 3D Scanner Data

We present a new technique for reconstructing a single shape and its non-rigid motion from 3D scanning data. Our algorithm takes a set of time-varying unstructured sample points that show partial views of a deforming object as input and reconstructs a single shape and a deformation field that fit the data. This representation yields dense correspondences for the whole sequence, as well as a completed 3D shape in every frame. In addition, the algorithm automatically removes spatial and temporal noise artifacts and outliers from the raw input data. Unlike previous methods, the algorithm does not require any shape template but computes a fitting shape automatically from the input data. Our reconstruction technique is based upon a novel topology aware adaptive sub-space deformation technique that allows handling long sequences with high resolution geometry efficiently. The algorithm accesses data in multiple sequential passes, so that long sequences can be streamed from hard disk, not being limited by main memory. We apply the technique to several benchmark data sets, increasing the complexity of the data that can be handled significantly in comparison to previous work, while at the same time improving the reconstruction quality

Combined RT-qPCR and pyrosequencing of a Spike glycoprotein polybasic cleavage motif can uncover pediatric SARS-CoV-2 infections associated with heterogeneous presentation

Background!#!Reverse transcription of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (+)RNA genome and subgenomic RNAs (sgRNAs) and subsequent quantitative polymerase chain reaction (RT-qPCR) is the reliable diagnostic gold standard for COVID-19 diagnosis and the identification of potential spreaders. Apart from clinical relevance and containment, for specific questions, it might be of interest to (re)investigate cases with low SARS-CoV-2 load, where RT-qPCR alone can deliver conflicting results, even though these cases might neither be clinically relevant nor significant for containment measures, because they might probably not be infectious. In order to expand the diagnostic bandwidth for non-routine questions, particularly for the reliable discrimination between negative and false-negative specimens associated with high C!##!Results!#!We successfully established a combined RT-qPCR and S-gene pyrosequencing method which can be optionally exploited after routine diagnostics. This allows a reliable interpretation of RT-qPCR results in specimens with relatively low viral loads and close to the detection limits of qPCR. After laboratory implementation, we tested the combined method in a large pediatric cohort from two German medical centers (n=769). Pyrosequencing after RT-qPCR enabled us to uncover 5 previously unrecognized cases of pediatric SARS-CoV-2-associated diseases, mainly exhibiting mild and heterogeneous presentation-apart from a single case of multisystem inflammatory syndrome in children (MIS-C) associated with SARS-CoV-2, who was hospitalized in the course of the study.!##!Conclusions!#!The proposed protocol allows a specific and sensitive confirmation of SARS-CoV-2 infections close to the detection limits of RT-qPCR. The tested biotinylated primers do not negatively affect the RT-qPCR pipeline and thus can be optionally applied to enable deeper inspection of RT-qPCR results by subsequent pyrosequencing. Moreover, due to the incremental transmission of SARS-CoV-2 variants of concern, we note that the used strategy can uncover (Spike) P681H allowing the pre-selection of SARS-CoV-2 B.1.1.7 candidate specimens for deep sequencing

High-harmonic generation enhancement with graphene heterostructures

We investigate high-harmonic generation in graphene heterostructures consisting of metallic nanoribbons separated from a graphene sheet by either a few-nanometer layer of aluminum oxide or an atomic monolayer of hexagonal boron nitride. The nanoribbons amplify the near-field at the graphene layer relative to the externally applied pumping, thus allowing us to observe third- and fifth-harmonic generation in the carbon monolayer at modest pump powers in the mid-infrared. We study the dependence of the nonlinear signals on the ribbon width and spacer thickness, as well as pump power and polarization, and demonstrate enhancement factors relative to bare graphene reaching 1600 and 4100 for third- and fifth-harmonic generation, respectively. Our work supports the use of graphene heterostructures to selectively enhance specific nonlinear processes of interest, an essential capability for the design of nanoscale nonlinear devices

Giant enhancement of third-harmonic generation in graphene-metal heterostructures

Nonlinear nanophotonics leverages engineered nanostructures to funnel light into small volumes and intensify nonlinear optical processes with spectral and spatial control. Due to its intrinsically large and electrically tunable nonlinear optical response, graphene is an especially promising nanomaterial for nonlinear optoelectronic applications. Here we report on exceptionally strong optical nonlinearities in graphene-insulator-metal heterostructures, demonstrating an enhancement by three orders of magnitude in the third-harmonic signal compared to bare graphene. Furthermore, by increasing the graphene Fermi energy through an external gate voltage, we find that graphene plasmons mediate the optical nonlinearity and modify the third-harmonic signal. Our findings show that graphene-insulator-metal is a promising heterostructure for optically-controlled and electrically-tunable nano-optoelectronic components