Search Results for author:
Found 16 papers, 9 papers with code
Points2Surf Learning Implicit Surfaces from Point Clouds
Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.
PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction
3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage, and engineering.
Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production
We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance -- where existing methods fall short.
Training and Predicting Visual Error for Real-Time Applications
Specifically, we train and deploy a neural network to estimate the visual error resulting from reusing shading or using reduced shading rates.
Assesment of material layers in building walls using GeoRadar
Assessing the structure of a building with non-invasive methods is an important problem.
Gaussian Mixture Convolution Networks
This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures.
Minimal Zeeman field requirement for a topological transition in superconductors
Platforms for creating Majorana quasiparticles rely on superconductivity and breaking of time-reversal symmetry.
Mesoscale and Nanoscale Physics
Electronic properties of InAs/EuS/Al hybrid nanowires
We study the electronic properties of InAs/EuS/Al heterostructures as explored in a recent experiment [S. Vaitiekenas \emph{et al.}, Nat.
Mesoscale and Nanoscale Physics
Conductance asymmetries in mesoscopic superconducting devices due to finite bias
It is important to identify the physical origin of conductance asymmetries: in contrast to other possible mechanisms such as quasiparticle poisoning, finite-bias effects are not detrimental to the performance of a topological qubit.
Mesoscale and Nanoscale Physics
Points2Surf: Learning Implicit Surfaces from Point Cloud Patches
Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.
Photorealistic Material Editing Through Direct Image Manipulation
In the proposed workflow, the user starts with an input image and applies a few intuitive transforms (e. g., colorization, image inpainting) within a 2D image editor of their choice, and in the next step, our technique produces a photorealistic result that approximates this target image.
Rendering Point Clouds with Compute Shaders
We propose a compute shader based point cloud rasterizer with up to 10 times higher performance than classic point-based rendering with the GL_POINT primitive.
Graphics
Next steps of quantum transport in Majorana nanowire devices
Majorana zero modes are localized quasiparticles that obey non-Abelian exchange statistics.
Mesoscale and Nanoscale Physics
Spin-Orbit Protection of Induced Superconductivity in Majorana Nanowires
Spin-orbit interaction (SOI) plays a key role in creating Majorana zero modes in semiconductor nanowires proximity coupled to a superconductor.
Mesoscale and Nanoscale Physics
Gaussian Material Synthesis
Workflow timings against Disney's "principled" shader reveal that our system scales well with the number of sought materials, thus empowering even novice users to generate hundreds of high-quality material models without any expertise in material modeling.
Kwant: a software package for quantum transport
Kwant is a Python package for numerical quantum transport calculations.
Mesoscale and Nanoscale Physics
