# Filtering After Shading With Stochastic Texture Filtering

arXiv: https://arxiv.org/abs/2407.06107 · HTML: https://mlsanity.com/paper/2407.06107/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 61 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 3 of 3)

- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Appearance-Driven Automatic 3D Model Simplification](https://mlsanity.com/paper/2104.03989/) (2021)
- [Random-Access Neural Compression of Material Textures](https://mlsanity.com/paper/2305.17105/) (2023)

## Built on by (most cited first, 5 of 5)

- [GPU-Friendly Laplacian Texture Blending](https://mlsanity.com/paper/2502.13945/) (2025)
- [Improved Stochastic Texture Filtering Through Sample Reuse](https://mlsanity.com/paper/2504.05562/) (2025)
- [FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting](https://mlsanity.com/paper/2511.23292/) (2025)
- [Collaborative Texture Filtering](https://mlsanity.com/paper/2506.17770/) (2025)
- [3DSS: 3D Surface Splatting for Inverse Rendering](https://mlsanity.com/paper/2605.05876/) (2026)

## 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 applying the texture filter after evaluating shading generally gives more accurate imagery than filtering textures before BSDF evaluation, as is current practice. These benefits are not merely theoretical, but are apparent in common cases. We demonstrate that practical and efficient filtering after shading is possible through the use of stochastic sampling of texture filters. 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 error from stochastic filtering is minimal. We find that this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.