238 research outputs found
Recommended from our members
High-Performance Integrated Window and Façade Solutions for California
The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California
BxDF material acquisition, representation, and rendering for VR and design
Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use. In this course, we describe the state of the art in material appearance acquisition and modelling, ranging from mathematical BSDFs to data-driven capture and representation of anisotropic materials, and volumetric/thread models for patterned fabrics. We further address the problem of material appearance constancy across different rendering platforms. We present two case studies in architectural and interior design. The first study demonstrates Yulio, a new platform for the creation, delivery, and visualization of acquired material models and reverse engineered cloth models in immersive VR experiences. The second study shows an end-to-end process of capture and data-driven BSDF representation using the physically-based Radiance system for lighting simulation and rendering
A custom designed density estimation method for light transport
We present a new Monte Carlo method for solving the global illumination problem in environments with general geometry descriptions and light emission and scattering properties. Current Monte Carlo global illumination algorithms are based on generic density estimation techniques that do not take into account any knowledge about the nature of the data points --- light and potential particle hit points --- from which a global illumination solution is to be reconstructed. We propose a novel estimator, especially designed for solving linear integral equations such as the rendering equation. The resulting single-pass global illumination algorithm promises to combine the flexibility and robustness of bi-directional path tracing with the efficiency of algorithms such as photon mapping
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
The variance reduction speed of physically-based rendering is heavily
affected by the adopted importance sampling technique. In this paper we propose
a novel online framework to learn the spatial-varying density model with a
single small neural network using stochastic ray samples. To achieve this task,
we propose a novel closed-form density model called the normalized anisotropic
spherical gaussian mixture, that can express complex irradiance fields with a
small number of parameters. Our framework learns the distribution in a
progressive manner and does not need any warm-up phases. Due to the compact and
expressive representation of our density model, our framework can be
implemented entirely on the GPU, allowing it produce high quality images with
limited computational resources
GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation
This paper presents a plugin that adds a representation of homogeneous and
heterogeneous, optically thick, translucent materials on the Blender 3D
modeling tool. The working principle of this plugin is based on a combination
of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based
subsurface scattering method (GenSSS). The proposed plugin has been implemented
using Mitsuba renderer, which is an open source rendering software. The
proposed plugin has been validated on measured subsurface scattering data. It's
shown that the proposed plugin visualizes homogeneous and heterogeneous
subsurface scattering effects, accurately, compactly and computationally
efficiently
BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs
Parametric Bidirectional Scattering Distribution Functions (BSDFs) are
pervasively used because of their flexibility to represent a large variety of
material appearances by simply tuning the parameters. While efficient
evaluation of parametric BSDFs has been well-studied, high-quality importance
sampling techniques for parametric BSDFs are still scarce. Existing sampling
strategies either heavily rely on approximations, resulting in high variance,
or solely perform sampling on a portion of the whole BSDF slice. Moreover, many
of the sampling approaches are specifically paired with certain types of BSDFs.
In this paper, we seek an efficient and general way for importance sampling
parametric BSDFs. We notice that the nature of importance sampling is the
mapping between a uniform distribution and the target distribution.
Specifically, when BSDF parameters are given, the mapping that performs
importance sampling on a BSDF slice can be simply recorded as a 2D image that
we name as importance map. Following this observation, we accurately precompute
the importance maps using a mathematical tool named optimal transport. Then we
propose a lightweight neural network to efficiently compress the precomputed
importance maps. In this way, we have brought parametric BSDF important
sampling to the precomputation stage, avoiding heavy runtime computation. Since
this process is similar to light baking where a set of images are precomputed,
we name our method importance baking. Together with a BSDF evaluation network
and a PDF (probability density function) query network, our method enables full
multiple importance sampling (MIS) without any revision to the rendering
pipeline. Our method essentially performs perfect importance sampling. Compared
with previous methods, we demonstrate reduced noise levels on rendering results
with a rich set of appearances
- …