Objects as volumes: A stochastic geometry view of opaque solids

Fuente: arXiv
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Autori principali: Miller, Bailey, Chen, Hanyu, Lai, Alice, Gkioulekas, Ioannis
Natura: Preprint
Pubblicazione: 2023
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author Miller, Bailey
Chen, Hanyu
Lai, Alice
Gkioulekas, Ioannis
author_facet Miller, Bailey
Chen, Hanyu
Lai, Alice
Gkioulekas, Ioannis
contents We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions, we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid, and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles, which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain, compare, and correct previous volumetric representations, as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.
format Preprint
id arxiv_https___arxiv_org_abs_2312_15406
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Objects as volumes: A stochastic geometry view of opaque solids
Miller, Bailey
Chen, Hanyu
Lai, Alice
Gkioulekas, Ioannis
Computer Vision and Pattern Recognition
Graphics
We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions, we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid, and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles, which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain, compare, and correct previous volumetric representations, as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.
title Objects as volumes: A stochastic geometry view of opaque solids
topic Computer Vision and Pattern Recognition
Graphics
url https://arxiv.org/abs/2312.15406