200,135 research outputs found
Three architectures for volume rendering
Volume rendering is a key technique in scientific visualization that lends itself to significant exploitable parallelism. The high computational demands of real-time volume rendering and continued technological advances in the area of VLSI give impetus to the development of special-purpose volume rendering architectures. This paper presents and characterizes three recently developed volume rendering engines which are based on the ray-casting method. A taxonomy of the algorithmic variants of ray-casting and details of each ray-casting architecture are discussed. The paper then compares the machine features and provides an outlook on future developments in the area of volume rendering hardware
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the
last few years, particularly empowered by the advances in differentiable
rendering and neural radiance fields. Real-time rendering is a highly desirable
goal for real-world applications. We propose HeadGaS, the first model to use 3D
Gaussian Splats (3DGS) for 3D head reconstruction and animation. In this paper
we introduce a hybrid model that extends the explicit representation from 3DGS
with a base of learnable latent features, which can be linearly blended with
low-dimensional parameters from parametric head models to obtain
expression-dependent final color and opacity values. We demonstrate that
HeadGaS delivers state-of-the-art results in real-time inference frame rates,
which surpasses baselines by up to ~2dB, while accelerating rendering speed by
over x10
A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps
In this paper we propose a solution to several key limitations of current web based mapping systems: slow rendering speeds and the restriction of online map viewing to a small number of areal units as well as a limited number of users. Our approach is implemented as a Scalable Tile Map Service that distributes dynamic choropleth maps in real-time through a new caching methodology. This new Map Service lays the foundation for advances in web based applications reliant on dynamic map rendering such as emergency management systems and interactive exploratory spatial data analysis. We present the results of an empirical illustration in which this new methodology is used to facilitate collaborative decision making by visualizing spatial outcomes of simulation results on the fly.
Evaluation of 3D Voxel Rendering Algorithms for Real-Time Interaction on a SIMD Graphics Processor
The display of three-dimensional medical data is becoming more common, but current hardware and image rendering algorithms do not generally allow real-time interaction with the image by the user. Real-time interactions, such as image rotation, utilize the motion processing capabilities of the human visual system, allowing a better understanding of the structures being imaged. Recent advances in general purpose graphics display equipment could make real-time interaction feasible in clinical setting. We have evaluated the capabilities of one type of advanced display architecture, the PIXAR Imaging Computer, for real-time interaction while displaying three-dimensional medical data as two-dimensional projections. It was discovered during this investigation that most suitable algorithms for implementation were based on the rendering of voxel rather than surface data. Two voxel-based techniques, back-to-front and front-to-back rendering produced acceptable, but not real-time performance. The quality of the images produced was not high, but allowed the determination of an image orientation which could then be used by a later high-quality rendering technique. Two conclusions were reached: first, the current performance of display hardware may allow acceptable interactive performance and produce high-quality images if a scheme of adaptive refinement is used wherein successively higher quality images are generated for the user. Second, the correct algorithm to use for fast rendering of volume data is highly dependent upon the architecture of the display processor, and in particular upon the ability of the processor to randomly access image data. If the processor is constrained to sequential or near sequential access to the voxel data, the choice of algorithms and the utilization of parallel processing is severely limited
Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering
Meeting online is becoming the new normal. Creating an immersive experience
for online meetings is a necessity towards more diverse and seamless
environments. Efficient photorealistic rendering of human 3D dynamics is the
core of immersive meetings. Current popular applications achieve real-time
conferencing but fall short in delivering photorealistic human dynamics, either
due to limited 2D space or the use of avatars that lack realistic interactions
between participants. Recent advances in neural rendering, such as the Neural
Radiance Field (NeRF), offer the potential for greater realism in metaverse
meetings. However, the slow rendering speed of NeRF poses challenges for
real-time conferencing. We envision a pipeline for a future extended reality
metaverse conferencing system that leverages monocular video acquisition and
free-viewpoint synthesis to enhance data and hardware efficiency. Towards an
immersive conferencing experience, we explore an accelerated NeRF-based
free-viewpoint synthesis algorithm for rendering photorealistic human dynamics
more efficiently. We show that our algorithm achieves comparable rendering
quality while performing training and inference 44.5% and 213% faster than
state-of-the-art methods, respectively. Our exploration provides a design basis
for constructing metaverse conferencing systems that can handle complex
application scenarios, including dynamic scene relighting with customized
themes and multi-user conferencing that harmonizes real-world people into an
extended world.Comment: Accepted to CVPR 2023 ECV Worksho
CWIPC-SXR: Point cloud dynamic human dataset for Social XR
Real-time, immersive telecommunication systems are quickly becoming a reality, thanks to the advances in acquisition, transmission, and rendering technologies. Point clouds in particular serve as a promising representation in these type of systems, offering photorealistic rendering capabilities with low complexity. Further development of transmission, coding, and quality evaluation algorithms, though, is currently hindered by the lack of publicly available datasets that represent realistic scenarios of remote communication between people in real-time.
Representing Volumetric Videos as Dynamic MLP Maps
This paper introduces a novel representation of volumetric videos for
real-time view synthesis of dynamic scenes. Recent advances in neural scene
representations demonstrate their remarkable capability to model and render
complex static scenes, but extending them to represent dynamic scenes is not
straightforward due to their slow rendering speed or high storage cost. To
solve this problem, our key idea is to represent the radiance field of each
frame as a set of shallow MLP networks whose parameters are stored in 2D grids,
called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all
frames. Representing 3D scenes with shallow MLPs significantly improves the
rendering speed, while dynamically predicting MLP parameters with a shared 2D
CNN instead of explicitly storing them leads to low storage cost. Experiments
show that the proposed approach achieves state-of-the-art rendering quality on
the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering
with a speed of 41.7 fps for images on an RTX 3090 GPU. The
code is available at https://zju3dv.github.io/mlp_maps/.Comment: Accepted to CVPR 2023. The first two authors contributed equally to
this paper. Project page: https://zju3dv.github.io/mlp_maps
LightSpeed: Light and Fast Neural Light Fields on Mobile Devices
Real-time novel-view image synthesis on mobile devices is prohibitive due to
the limited computational power and storage. Using volumetric rendering
methods, such as NeRF and its derivatives, on mobile devices is not suitable
due to the high computational cost of volumetric rendering. On the other hand,
recent advances in neural light field representations have shown promising
real-time view synthesis results on mobile devices. Neural light field methods
learn a direct mapping from a ray representation to the pixel color. The
current choice of ray representation is either stratified ray sampling or
Plucker coordinates, overlooking the classic light slab (two-plane)
representation, the preferred representation to interpolate between light field
views. In this work, we find that using the light slab representation is an
efficient representation for learning a neural light field. More importantly,
it is a lower-dimensional ray representation enabling us to learn the 4D ray
space using feature grids which are significantly faster to train and render.
Although mostly designed for frontal views, we show that the light-slab
representation can be further extended to non-frontal scenes using a
divide-and-conquer strategy. Our method offers superior rendering quality
compared to previous light field methods and achieves a significantly improved
trade-off between rendering quality and speed.Comment: Project Page: http://lightspeed-r2l.github.io/ . Add camera ready
versio
Efficient Methods for Computational Light Transport
En esta tesis presentamos contribuciones sobre distintos retos computacionales relacionados con transporte de luz. Los algoritmos que utilizan información sobre el transporte de luz están presentes en muchas aplicaciones de hoy en día, desde la generación de efectos visuales, a la detección de objetos en tiempo real. La luz es una valiosa fuente de información que nos permite entender y representar nuestro entorno, pero obtener y procesar esta información presenta muchos desafíos debido a la complejidad de las interacciones entre la luz y la materia. Esta tesis aporta contribuciones en este tema desde dos puntos de vista diferentes: algoritmos en estado estacionario, en los que se asume que la velocidad de la luz es infinita; y algoritmos en estado transitorio, que tratan la luz no solo en el dominio espacial, sino también en el temporal. Nuestras contribuciones en algoritmos estacionarios abordan problemas tanto en renderizado offline como en tiempo real. Nos enfocamos en la reducción de varianza para métodos offline,proponiendo un nuevo método para renderizado eficiente de medios participativos. En renderizado en tiempo real, abordamos las limitacionesde consumo de batería en dispositivos móviles proponiendo un sistema de renderizado que incrementa la eficiencia energética en aplicaciones gráficas en tiempo real. En el transporte de luz transitorio, formalizamos la simulación de este tipo transporte en este nuevo dominio, y presentamos nuevos algoritmos y métodos para muestreo eficiente para render transitorio. Finalmente, demostramos la utilidad de generar datos en este dominio, presentando un nuevo método para corregir interferencia multi-caminos en camaras Timeof- Flight, un problema patológico en el procesamiento de imágenes transitorias.n this thesis we present contributions to different challenges of computational light transport. Light transport algorithms are present in many modern applications, from image generation for visual effects to real-time object detection. Light is a rich source of information that allows us to understand and represent our surroundings, but obtaining and processing this information presents many challenges due to its complex interactions with matter. This thesis provides advances in this subject from two different perspectives: steady-state algorithms, where the speed of light is assumed infinite, and transient-state algorithms, which deal with light as it travels not only through space but also time. Our steady-state contributions address problems in both offline and real-time rendering. We target variance reduction in offline rendering by proposing a new efficient method for participating media rendering. In real-time rendering, we target energy constraints of mobile devices by proposing a power-efficient rendering framework for real-time graphics applications. In transient-state we first formalize light transport simulation under this domain, and present new efficient sampling methods and algorithms for transient rendering. We finally demonstrate the potential of simulated data to correct multipath interference in Time-of-Flight cameras, one of the pathological problems in transient imaging.<br /
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
In this paper we are extracting surface reflectance and natural environmental
illumination from a reflectance map, i.e. from a single 2D image of a sphere of
one material under one illumination. This is a notoriously difficult problem,
yet key to various re-rendering applications. With the recent advances in
estimating reflectance maps from 2D images their further decomposition has
become increasingly relevant.
To this end, we propose a Convolutional Neural Network (CNN) architecture to
reconstruct both material parameters (i.e. Phong) as well as illumination (i.e.
high-resolution spherical illumination maps), that is solely trained on
synthetic data. We demonstrate that decomposition of synthetic as well as real
photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the
first time, on Low Dynamic Range (LDR) as well. Results are compared to
previous approaches quantitatively as well as qualitatively in terms of
re-renderings where illumination, material, view or shape are changed.Comment: Stamatios Georgoulis and Konstantinos Rematas contributed equally to
this wor
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