mlsanityData to 14 Oct 2026

Objects as volumes: A stochastic geometry view of opaque solids

arXiv 2312.15406 ↗First posted 24 Dec 2023cs.CV cs.GRcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 25 of its 67 references that are in the map. First posted on arXiv 24 Dec 2023.

Builds on

25 of 67 references.

References · 25

  1. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  2. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  3. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  4. 2021Volume Rendering of Neural Implicit Surfacescited by 996
  5. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
  6. 2020Implicit Geometric Regularization for Learning Shapescited by 710
  7. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 700
  8. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  9. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 600
  10. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
  11. 2016Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networkscited by 543
  12. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 490
13 more
  1. 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 470
  2. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 390
  3. 2021Neural RGB-D Surface Reconstructioncited by 313
  4. 2021Urban Radiance Fieldscited by 263
  5. 2022Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstructioncited by 212
  6. 2017Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistencycited by 177
  7. 2022NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priorscited by 125
  8. 2023Adaptive Shells for Efficient Neural Radiance Field Renderingcited by 69
  9. 2022Stochastic Poisson Surface Reconstructioncited by 13
  10. 2018A Radiative Transfer Framework for Spatially-Correlated Materialscited by 7
  11. 2023Neural Stochastic Screened Poisson Reconstructioncited by 7
  12. 2021An unbiased ray-marching transmittance estimatorcited by 5
  13. 2018A reciprocal formulation of non-exponential radiative transfer. 1: Sketch and motivationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 2; 2026: 6.

15 papers.

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