research-article

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Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis

Authors:

Christian Reiser,

Stephan Garbin,

Pratul Srinivasan,

Richard Szeliski,

Ben Mildenhall,

Jonathan Barron,

Andreas GeigerAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 43, Issue 4

Article No.: 149, Pages 1 - 14

https://doi.org/10.1145/3658130

Published: 19 July 2024 Publication History

Abstract

While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry in a "fuzzy" manner, which hinders exact localization of the surface. In this work, we modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. First, we employ a discrete opacity grid representation instead of a continuous density field, which allows opacity values to discontinuously transition from zero to one at the surface. Second, we anti-alias by casting multiple rays per pixel, which allows occlusion boundaries and subpixel structures to be modelled without using semi-transparent voxels. Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training. Lastly, we develop a fusion-based meshing strategy followed by mesh simplification and appearance model fitting. The compact meshes produced by our model can be rendered in real-time on mobile devices and achieve significantly higher view synthesis quality compared to existing mesh-based approaches. Our interactive webdemo is available at https://binary-opacity-grid.github.io.

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Cited By

Steiner MKöhler TRadl LSteinberger M(2024)Frustum Volume Caching for Accelerated NeRF RenderingProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753707:3(1-22)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3675370
Radl LKurz ASteiner MSteinberger M(2024)Analyzing the Internals of Neural Radiance Fields2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00288(2822-2831)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00288
Wu QZheng JCai J(2024)Surface Reconstruction from 3D Gaussian Splatting via Local Structural HintsComputer Vision – ECCV 202410.1007/978-3-031-72627-9_25(441-458)Online publication date: 20-Oct-2024
https://doi.org/10.1007/978-3-031-72627-9_25

Index Terms

Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
      2. Computer vision representations
        Appearance and texture representations
  2. Computer graphics
    1. Rendering
      1. Rasterization

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 43, Issue 4

July 2024

1774 pages

EISSN:1557-7368

DOI:10.1145/3675116

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Published: 19 July 2024

Published in TOG Volume 43, Issue 4

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Cited By

Steiner MKöhler TRadl LSteinberger M(2024)Frustum Volume Caching for Accelerated NeRF RenderingProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753707:3(1-22)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3675370
Radl LKurz ASteiner MSteinberger M(2024)Analyzing the Internals of Neural Radiance Fields2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00288(2822-2831)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00288
Wu QZheng JCai J(2024)Surface Reconstruction from 3D Gaussian Splatting via Local Structural HintsComputer Vision – ECCV 202410.1007/978-3-031-72627-9_25(441-458)Online publication date: 20-Oct-2024
https://doi.org/10.1007/978-3-031-72627-9_25

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