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

A radiative transfer framework for non-exponential media

We develop a new theory of volumetric light transport for media with non-exponential free-flight distributions. Recent insights from atmospheric sciences and neutron transport demonstrate that such distributions arise in the presence of correlated scatterers, which are naturally produced by processes such as cloud condensation and fractal-pattern formation. Our theory accommodates correlations by disentangling the concepts of the free-flight distribution and transmittance, which are equivalent when scatterers are statistically independent, but become distinct when correlations are present. Our theory results in a generalized path integral which allows us to handle non-exponential media using the full range of Monte Carlo rendering algorithms while enriching the range of achievable appearance. We propose parametric models for controlling the statistical correlations by leveraging work on stochastic processes, and we develop a method to combine such unresolved correlations (and the resulting non-exponential free-flight behavior) with explicitly modeled macroscopic heterogeneity. This provides a powerful authoring approach where artists can freely design the shape of the attenuation profile separately from the macroscopic heterogeneous density, while our theory provides a physically consistent interpretation in terms of a path space integral. We address important considerations for graphics including energy conservation, reciprocity, and bidirectional rendering algorithms, all in the presence of surfaces and correlated media

An approximation to multiple scattering in volumetric illumination towards real-time rendering

Many volumetric illumination techniques for volume rendering were developed through out the years. However, there are still many constraints regarding the computation of multiple scattering path tracing in real-time applications due to its natural complexity and scale. Path tracing with multiple scattering support can produce physically correct results but suffers from noise and low convergence rates. This work proposes a new real-time algorithm to approximate multiple scattering, usually only available in offline rendering production, to real-time. Our approach explores the human perceptual system to speed up computation. Given two images, we use a CIE metric stating that the two will be perceived as similar to the human eye if the Euclidean distance between the two images in CIELAB color space is smaller than 2.3. Hence, we use this premise to guide our in vestigations when changing ray and bounce parameters in our renderer. Our results show that we can reduce from 105 to 104 Samples Per Pixel (SPP) with a negligible perceptual difference between both results, allowing us to cut rendering times by 10 whenever we divide SPP by 10. Similarly, we can reduce the number of bounces from 1000 to 100 with a negligible perceptual difference while reducing rendering times by almost half. We also propose a new algorithm in real-time, Lobe Estimator, that approximates these behaviors and parameters while performing twice as faster as the classic Ray Marching technique.Muitas técnicas de ilmuninação volumétrica foram desenvolvidas ao longo dos anos. Entretanto, ainda há muitas restrições na computação de multiple scattering em aplicações de tempo real usando path tracing, devido à sua complexidade e escala. Path tracing com suporte a multiple scattering é capaz de produzir resultados fisicamente corretos, mas sofre de ruídos e baixa taixa de convergência. Portanto, este trabalho propõe um novo algoritmo de tempo real para aproximar multiple scattering, usado em offline rendering. Nossa abordagem irá explorar o sistema perceptual visual humano para acelerar a computação. A partir de duas imagens, nós usamos a métrica da CIE que afirma que duas imagens são percebidas como similar ao olho humano se a distância Euclidiana das duas imagens no espaço de cores CIELAB for menor que 2.3. Dessa forma, nós usamos essa premissa para guiar nossas investigações quando alterando os parâmetros de Samples Per Pixel (SPP) e bounces nos renderizadores. Nossos resultados mostram que podemos redu zir de 105 para 104 Samples Per Pixel (SPP) com uma diferença perceptual negligenciável entre ambos paramêtros, permitindo reduzir o tempo de renderização por 10 a cada vez que dividimos o SPP por 10. Similarmente, nós podemos reduzir o número de bounces de 1000 para 100 com uma diferença perceptual negligenciável, enquanto reduzindo o tempo de renderização por quase metade. Nós também propusemos um novo algoritmo em tempo real, Lobe Estimator, que permite aproximar esses comportamentos e paramê tros enquanto permformando duas vezes mais rápido que o clássico Ray Marching

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data