mlsanityData to 14 Oct 2026

Stochastic Texture Filtering

arXiv 2305.05810 ↗First posted 9 May 2023cs.GR cs.CVcited by 1

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Abstract (arXiv)

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that filtering textures after evaluating lighting, rather than before BSDF evaluation as is current practice, gives a more accurate solution to the rendering equation. These benefits are not merely theoretical, but are apparent in common cases. We further show that stochastically sampling texture filters is crucial for enabling this approach, which has not been possible previously except in limited cases. Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional stochastic error is minimal. Furthermore, this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 79 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

2 of 79 references.

References · 2

  1. 2019Handheld Multi-Frame Super-Resolutioncited by 85
  2. 2022NeuralVDB: High-resolution Sparse Volume Representation using Hierarchical Neural Networkscited by 20

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Citing papers per year: 2024: 1.

1 paper.