1,038 research outputs found
Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres
Interactions between clouds and radiation are at the root of many
difficulties in numerically predicting future weather and climate and in
retrieving the state of the atmosphere from remote sensing observations. The
large range of issues related to these interactions, and in particular to
three-dimensional interactions, motivated the development of accurate radiative
tools able to compute all types of radiative metrics, from monochromatic, local
and directional observables, to integrated energetic quantities. In the
continuity of this community effort, we propose here an open-source library for
general use in Monte Carlo algorithms. This library is devoted to the
acceleration of path-tracing in complex data, typically high-resolution
large-domain grounds and clouds. The main algorithmic advances embedded in the
library are those related to the construction and traversal of hierarchical
grids accelerating the tracing of paths through heterogeneous fields in
null-collision (maximum cross-section) algorithms. We show that with these
hierarchical grids, the computing time is only weakly sensitivive to the
refinement of the volumetric data. The library is tested with a rendering
algorithm that produces synthetic images of cloud radiances. Two other examples
are given as illustrations, that are respectively used to analyse the
transmission of solar radiation under a cloud together with its sensitivity to
an optical parameter, and to assess a parametrization of 3D radiative effects
of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2
An AR Enhancement of Printed Educational Resources: Keeping printed educational materials competitive in a digital age
Digital resources have begun to take over our educational system and overshadow traditional printed resources. While these digital resources are becoming increasingly popular research has shown that printed resources have notable benefits that should not be dismissed. Enhancing these printed resources with augmented reality (AR) technology will allow them to be competitive with digital resources while preserving their academic benefits. The readily available smart devices carried in the pockets of most students have already prepared many classrooms for the use of AR. The interactive and visual potential of AR makes it an appealing educational tool that can dramatically improve the experience of learning with printed resources. This project will utilize 3D and animation graphics to simulate the use of AR on a selected set of existing texts
An audio-visual system for object-based audio : from recording to listening
Object-based audio is an emerging representation for
audio content, where content is represented in a reproduction format-agnostic way and, thus, produced once for consumption on many different kinds of devices. This affords new opportunities for immersive, personalized, and interactive listening experiences. This paper introduces an end-to-end object-based spatial audio pipeline, from sound recording to listening. A high-level system architecture is proposed, which includes novel audiovisual interfaces to support object-based capture and listenertracked rendering, and incorporates a proposed component for objectification, that is, recording content directly into an object-based form. Text-based and extensible metadata enable communication between the system components. An open architecture for object rendering is also proposed. The system’s capabilities are evaluated in two parts. First, listener-tracked reproduction of metadata automatically estimated from two moving talkers is evaluated using an objective binaural localization model. Second, object-based scene capture with audio extracted using blind source separation (to remix between two talkers) and beamforming (to remix a recording of a jazz group) is evaluate
Analyzing and Developing Aspects of the Artist Pipeline for Clemson University Art
Major digital production facilities such as Sony Pictures Imageworks, Pixar Animation studio, Walt Disney Animation Studio, and Epic Games use a production system called a pipeline. The term “pipeline” refers to the structure and process of data flow between the various phases of production from story to final edit. This paper examines current production pipeline practices in the Digital Production Arts program at Clemson University and proposes updates and modifications to the workflow. Additionally, this thesis suggests tools that are intended to improve the pipeline with artist-friendly interfaces and customizable integration between software and remote-production capabilities
Um ambiente para desevonvoimento de algoritmos de amostragem e remoção de ruĂdo
In the context of Monte Carlo rendering, although many sampling and denoising techniques have been proposed in the last few years, the case for which one should be used for a specific scene is still to be made. Moreover, developing a new technique has required selecting a particular rendering system, which makes the technique tightly coupled to the chosen renderer and limits the amount of scenes it can be tested on. In this work, we propose a renderer-agnostic framework for developing and benchmarking sampling and denoising techniques for Monte Carlo rendering. It decouples techniques from rendering systems by hiding the renderer details behind a general API. This improves productivity and allows for direct comparisons among techniques using scenes from different rendering systems. The proposed framework contains two main parts: a software development kit that helps users to develop and and test their techniques locally, and an online system that allows users to submit their techniques and have them automatically benchmarked on our servers. We demonstrate its effectiveness by using our API to instrument four rendering systems and a variety of Monte Carlo denoising techniques — including recent learning-based ones — and performing a benchmark across different rendering systems.No contexto de Monte Carlo rendering, apesar de diversas tĂ©cnicas de amostragem e remoção de ruĂdo tenham sido propostas nos Ăşltimos anos, aportar qual tĂ©cnica deve ser usada para uma cena especĂfica ainda Ă© uma tarefa difĂcil. AlĂ©m disso, desenvolver uma nova tĂ©cnica requer escolher um renderizador em particular, o que torna a tĂ©cnica dependente do renderizador escolhido e limita a quantidade de cenas disponĂveis para testar a tĂ©cnica. Neste trabalho, um framework para desenvolvimento e avaliação de tĂ©cnicas de amostragem e remoção de ruĂdo para Monte Carlo rendering Ă© proposto. Ele permite desacoplar as tĂ©cnicas dos renderizadores por meio de uma API genĂ©rica, promovendo a reprodutibilidade e permitindo comparações entre tĂ©cnicas utilizando-se cenas de diferentes renderizadores. O sistema proposto contĂ©m duas partes principais: um kit de desenvolvimento de software que ajuda os usuários a desenvolver e testar suas tĂ©cnicas localmente, e um sistema online que permite que usuários submetam tĂ©cnicas para que as mesmas sejam automaticamente avaliadas no nosso servidor. Para demonstramos a efetividade do ambiante proposto, modificamos quatro renderizadores e várias tĂ©cnicas de remoção de ruĂdo — incluindo tĂ©cnicas recentes baseadas em aprendizado de máquina — e efetuamos uma avaliação utilizando cenas de diferentes renderizadores
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