Hi-fi

Résumé

Hi-fi ou hifi est l'abréviation du terme anglophone « en », qui signifie en français « haute-fidélité », utilisé dans l'électronique grand public ou l'électroacoustique. Ce terme est censé indiquer que le matériel paré de cette qualification offre une qualité supérieure aux équipements courants. Le but étant d'obtenir une reproduction sonore aussi proche que possible de l'original. Contrairement à une opinion largement répandue, les appareils « hi-fi » ne doivent pas répondre impérativement à des « normes hi-fi » : si des tentatives pour imposer des normes ont existé dans le passé, aucune vérification des performances n'est réalisée par un organisme officiel. L'idée même de « norme Hi-fi » est peu satisfaisante : le but des normes est généralement d'assurer la sécurité et l'interopérabilité des équipements, d'indiquer des méthodes de mesure, pas d'exiger un niveau de performance, ici difficile à définir et devant être réactualisé sans cesse. Le terme « hifi » peut donc être utilisé pra

Source officielle

https://fr.wikipedia.org/wiki/Hi-fi

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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Andreas Geiger, Angelos Katharopoulos, Despoina Paschalidou

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.

IEEE COMPUTER SOC2021

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AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

Majed El Helou, Muhammad Haris, Wei Li, Hanwen Liu, Radu Timofte, Xin Yang, Peng Yi, Jia Yu, Ruofan Zhou

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image. Compared to previous, this challenge focuses on an extreme upscaling factor, x16, and employs the novel DIVerse 8K resolution (DIV8K) dataset. This report focuses on the proposed solutions and final results. The challenge had 2 tracks. The goal in Track 1 was to generate a super-resolution result with high fidelity, using the conventional PSNR as the primary metric to evaluate different methods. Track 2 instead focused on generating visually more pleasant super-resolution results, evaluated using subjective opinions. The two tracks had 71 and 52 registered participants, respectively, and 9 teams competed in the final testing phase. This report gauges the experimental protocol and baselines for the extreme image super-resolution task.

IEEE COMPUTER SOC2019

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Frozen, the movie, released in 2013, made a big impression on many people: some children were no doubt amazed by the talking snowman, others by the film’s music, the designers were probably more interesed in the reproduction quality of the snow modelled, by the Material Point Method (MPM). Many paperspraise the effectiveness of this method for simulating complex phenomena. However, very few studies have been carried out to verify the method on very simple academic problems; that is precisely what we will try to study throughout this paper. We will see that simple problem modeling is not so easy with MPM. We will try to present as well as possible the different factors influencing the results, before concluding on its advantages and disadvantages

2020

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