117 research outputs found
Estimating performance of an ray- tracing ASIC design
Journal ArticleRecursive ray tracing is a powerful rendering technique used to compute realistic images by simulating the global light transport in a scene. Algorithmic improvements and FPGA-based hardware implementations of ray tracing have demonstrated realtime performance but hardware that achieves performance levels comparable to commodity rasterization graphics chips is still not available. This paper describes the architecture and ASIC implementations of the DRPU design (Dynamic Ray Processing Unit) that closes this performance gap. The DRPU supports fully programmable shading and most kinds of dynamic scenes and thus provides similar capabilities as current GPUs. It achieves high efficiency due to SIMD processing of floating point vectors, massive multithreading, synchronous execution of packets of threads, and careful management of caches for scene data. To support dynamic scenes B-KD trees are used as spatial index structures that are processed by a custom traversal and intersection unit and modified by an Update Processor on scene changes
Interactive isosurface ray tracing of time-varying tetrahedral volumes
Journal ArticleAbstract- We describe a system for interactively rendering isosurfaces of tetrahedral finite-element scalar fields using coherent ray tracing techniques on the CPU. By employing state-of-the art methods in polygonal ray tracing, namely aggressive packet/frustum traversal of a bounding volume hierarchy, we can accomodate large and time-varying unstructured data. In conjunction with this efficiency structure, we introduce a novel technique for intersecting ray packets with tetrahedral primitives. Ray tracing is flexible, allowing for dynamic changes in isovalue and time step, visualization of multiple isosurfaces, shadows, and depth-peeling transparency effects. The resulting system offers the intuitive simplicity of isosurfacing, guaranteed-correct visual results, and ultimately a scalable, dynamic and consistently interactive solution for visualizing unstructured volumes
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations
from a set of reduced parameters. A convolutional neural network is trained on
a collection of discrete, parameterizable fluid simulation velocity fields. Due
to the capability of deep learning architectures to learn representative
features of the data, our generative model is able to accurately approximate
the training data set, while providing plausible interpolated in-betweens. The
proposed generative model is optimized for fluids by a novel loss function that
guarantees divergence-free velocity fields at all times. In addition, we
demonstrate that we can handle complex parameterizations in reduced spaces, and
advance simulations in time by integrating in the latent space with a second
network. Our method models a wide variety of fluid behaviors, thus enabling
applications such as fast construction of simulations, interpolation of fluids
with different parameters, time re-sampling, latent space simulations, and
compression of fluid simulation data. Reconstructed velocity fields are
generated up to 700x faster than re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019),
additional materials: http://www.byungsoo.me/project/deep-fluids
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
Precomputed Radiance Transfer (PRT) remains an attractive solution for
real-time rendering of complex light transport effects such as glossy global
illumination. After precomputation, we can relight the scene with new
environment maps while changing viewpoint in real-time. However, practical PRT
methods are usually limited to low-frequency spherical harmonic lighting.
All-frequency techniques using wavelets are promising but have so far had
little practical impact. The curse of dimensionality and much higher data
requirements have typically limited them to relighting with fixed view or only
direct lighting with triple product integrals. In this paper, we demonstrate a
hybrid neural-wavelet PRT solution to high-frequency indirect illumination,
including glossy reflection, for relighting with changing view. Specifically,
we seek to represent the light transport function in the Haar wavelet basis.
For global illumination, we learn the wavelet transport using a small
multi-layer perceptron (MLP) applied to a feature field as a function of
spatial location and wavelet index, with reflected direction and material
parameters being other MLP inputs. We optimize/learn the feature field
(compactly represented by a tensor decomposition) and MLP parameters from
multiple images of the scene under different lighting and viewing conditions.
We demonstrate real-time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed
rendering of challenging scenes involving view-dependent reflections and even
caustics.Comment: 13 pages, 9 figures, to appear in cgf proceedings of egsr 202
Compact 3D Gaussian Representation for Radiance Field
Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in
capturing complex 3D scenes with high fidelity. However, one persistent
challenge that hinders the widespread adoption of NeRFs is the computational
bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian
splatting (3DGS) has recently emerged as an alternative representation that
leverages a 3D Gaussisan-based representation and adopts the rasterization
pipeline to render the images rather than volumetric rendering, achieving very
fast rendering speed and promising image quality. However, a significant
drawback arises as 3DGS entails a substantial number of 3D Gaussians to
maintain the high fidelity of the rendered images, which requires a large
amount of memory and storage. To address this critical issue, we place a
specific emphasis on two key objectives: reducing the number of Gaussian points
without sacrificing performance and compressing the Gaussian attributes, such
as view-dependent color and covariance. To this end, we propose a learnable
mask strategy that significantly reduces the number of Gaussians while
preserving high performance. In addition, we propose a compact but effective
representation of view-dependent color by employing a grid-based neural field
rather than relying on spherical harmonics. Finally, we learn codebooks to
compactly represent the geometric attributes of Gaussian by vector
quantization. With model compression techniques such as quantization and
entropy coding, we consistently show over 25 reduced storage and
enhanced rendering speed, while maintaining the quality of the scene
representation, compared to 3DGS. Our work provides a comprehensive framework
for 3D scene representation, achieving high performance, fast training,
compactness, and real-time rendering. Our project page is available at
https://maincold2.github.io/c3dgs/.Comment: Project page: http://maincold2.github.io/c3dgs
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