Visual Studio Code | EPFL Graph Search

Visual Studio Code, also commonly referred to as VS Code, is a source-code editor made by Microsoft with the Electron Framework, for Windows, Linux and macOS. Features include support for debugging, syntax highlighting, intelligent code completion, snippets, code refactoring, and embedded Git. Users can change the theme, keyboard shortcuts, preferences, and install extensions that add functionality. In the Stack Overflow 2023 Developer Survey, Visual Studio Code was ranked the most popular developer environment tool among 86,544 respondents, with 73.71% reporting that they use it. It increased its use among those learning to code versus those developing as a profession (78% vs. 74%). History Visual Studio Code was first announced on April 29, 2015, by Microsoft at the 2015 Build conference. A preview build was released shortly thereafter. On November 18, 2015, the source of Visual Studio Code was released under the MIT License and made available on GitHub. Extension supp

Geometric Deep Learning for Volumetric Computational Fluid Dynamics

Luca Zampieri

This master thesis explores ways to apply geometric deep learning to the field of numerical simulations with an emphasis on the Navier-Stokes equations. With the recent success of Deep Learning, there should be room for experimentation also in the field of fluid simulations. Here we lead such an experiment. We propose an end-to-end differentiable architecture that allow object-to-mesh predictions of fluid simulations. We provide a comparison with a baseline and visual results on three different datasets: airfoils, backward facing steps and winged drones.

2019

Graph-Based Interpolation for Zooming in 3D Scenes

Pinar Akyazi, Pascal Frossard

In multiview systems, color plus depth format builds 3D representations of scenes within which the users can freely navigate by changing their viewpoints. In this paper we present a framework for view synthesis when the user requests an arbitrary viewpoint that is closer to the 3D scene than the reference image. On the target image plane, the requested view obtained via depth-image-based-rendering (DIBR) is irregularly structured and has missing information due to the expansion of objects. We propose a novel framework that adopts a graph-based representation of the target view in order to interpolate the missing image pixels under sparsity priors. More specifically, we impose that the target image is reconstructed with a few atoms of a graph-based dictionary. Experimental results show that the reconstructed views have better PSNR and MSSIM quality than the ones generated within the same framework with analytical dictionaries, and are comparable to the ones reconstructed with TV regularization and linear interpolation on graphs. Visual results, however, show that our method better preserves the details and results in fewer disturbing artifacts than the other interpolation methods.

2017

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Pinar Akyazi, Touradj Ebrahimi

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality level, to enable efficient storage and transmission. Deep learning (DL) image coding solutions, typically using an auto-encoder architecture, promise significant improvements in compression efficiency. These methods adopt a novel coding approach where the encoder-decoder architecture is mostly based on neural networks, notably with analysis and synthesis transforms learned from a large amount of training data and an appropriate loss function. There are limited amount of works targeting the subjective evaluation of DL learning-based image coding solutions compression performance. Since learning-based image codecs use complex and highly non-linear generative models, very different artifacts are present in the decoded images, when compared to conventional artifacts such as blockiness, blurring and ringing distortions typical of traditional DCT block-based and wavelet image coding. In this context, the main objective of this paper is to review, characterize and evaluate some of the most relevant learning-based image coding solutions in the literature. Regarding the subjective quality evaluation, the assessment tests were conducted during the 84th JPEG meeting in Brussels, Belgium, by a mix of experts and naive observers. These subjective tests evaluated the performance of five state-of-the-art learning-based image coding solutions against four conventional, standard image coding (HEVC, WebP, JPEG 2000 and JPEG), applied to eight natural images, at four different coding bitrates. The experimental results obtained show that the subjective quality obtained with the selected learning-based image coding solution are competitive with conventional codecs. Moreover, a thorough inspection on the visual results has revealed some of the typical artifacts encountered in the learning -based image coding.

SPIE-INT SOC OPTICAL ENGINEERING2021

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Pinar Akyazi, Touradj Ebrahimi

SPIE-INT SOC OPTICAL ENGINEERING2021