Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media

Fuente: arXiv
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Main Authors: Condor, Jorge, Speierer, Sebastien, Bode, Lukas, Bozic, Aljaz, Green, Simon, Didyk, Piotr, Jarabo, Adrian
Format: Preprint
Published: 2024
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_version_ 1866911143279198208
author Condor, Jorge
Speierer, Sebastien
Bode, Lukas
Bozic, Aljaz
Green, Simon
Didyk, Piotr
Jarabo, Adrian
author_facet Condor, Jorge
Speierer, Sebastien
Bode, Lukas
Bozic, Aljaz
Green, Simon
Didyk, Piotr
Jarabo, Adrian
contents Efficient scene representations are essential for many computer graphics applications. A general unified representation that can handle both surfaces and volumes simultaneously, remains a research challenge. Inspired by recent methods for scene reconstruction that leverage mixtures of 3D Gaussians to model radiance fields, we formalize and generalize the modeling of scattering and emissive media using mixtures of simple kernel-based volumetric primitives. We introduce closed-form solutions for transmittance and free-flight distance sampling for different kernels, and propose several optimizations to use our method efficiently within any off-the-shelf volumetric path tracer. We demonstrate our method as a compact and efficient alternative to other forms of volume modeling for forward and inverse rendering of scattering media. Furthermore, we adapt and showcase our method in radiance field optimization and rendering, providing additional flexibility compared to current state of the art given its ray-tracing formulation. We also introduce the Epanechnikov kernel and demonstrate its potential as an efficient alternative to the traditionally-used Gaussian kernel in scene reconstruction tasks. The versatility and physically-based nature of our approach allows us to go beyond radiance fields and bring to kernel-based modeling and rendering any path-tracing enabled functionality such as scattering, relighting and complex camera models.
format Preprint
id arxiv_https___arxiv_org_abs_2405_15425
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media
Condor, Jorge
Speierer, Sebastien
Bode, Lukas
Bozic, Aljaz
Green, Simon
Didyk, Piotr
Jarabo, Adrian
Graphics
Computer Vision and Pattern Recognition
I.3.2; I.3.3; I.3.6; I.3.5; I.3.7
Efficient scene representations are essential for many computer graphics applications. A general unified representation that can handle both surfaces and volumes simultaneously, remains a research challenge. Inspired by recent methods for scene reconstruction that leverage mixtures of 3D Gaussians to model radiance fields, we formalize and generalize the modeling of scattering and emissive media using mixtures of simple kernel-based volumetric primitives. We introduce closed-form solutions for transmittance and free-flight distance sampling for different kernels, and propose several optimizations to use our method efficiently within any off-the-shelf volumetric path tracer. We demonstrate our method as a compact and efficient alternative to other forms of volume modeling for forward and inverse rendering of scattering media. Furthermore, we adapt and showcase our method in radiance field optimization and rendering, providing additional flexibility compared to current state of the art given its ray-tracing formulation. We also introduce the Epanechnikov kernel and demonstrate its potential as an efficient alternative to the traditionally-used Gaussian kernel in scene reconstruction tasks. The versatility and physically-based nature of our approach allows us to go beyond radiance fields and bring to kernel-based modeling and rendering any path-tracing enabled functionality such as scattering, relighting and complex camera models.
title Don't Splat your Gaussians: Volumetric Ray-Traced Primitives for Modeling and Rendering Scattering and Emissive Media
topic Graphics
Computer Vision and Pattern Recognition
I.3.2; I.3.3; I.3.6; I.3.5; I.3.7
url https://arxiv.org/abs/2405.15425