# A Bayesian Inference Framework for Procedural Material Parameter Estimation

arXiv: https://arxiv.org/abs/1912.01067 · HTML: https://mlsanity.com/paper/1912.01067/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [MatFusion: A Generative Diffusion Model for SVBRDF Capture](https://mlsanity.com/paper/2406.06539/) (2024)
- [TileGen: Tileable, Controllable Material Generation and Capture](https://mlsanity.com/paper/2206.05649/) (2022)
- [PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes](https://mlsanity.com/paper/2207.00757/) (2022)
- [UMat: Uncertainty-Aware Single Image High Resolution Material Capture](https://mlsanity.com/paper/2305.16312/) (2023)
- [DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation](https://mlsanity.com/paper/2412.15200/) (2024)
- [One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns](https://mlsanity.com/paper/2404.16292/) (2024)
- [Robust Inverse Graphics via Probabilistic Inference](https://mlsanity.com/paper/2402.01915/) (2024)
- [Woven Fabric Capture with a Reflection-Transmission Photo Pair](https://mlsanity.com/paper/2406.19398/) (2024)
- [Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner](https://mlsanity.com/paper/2502.14462/) (2025)
- [DualMat: PBR Material Estimation via Coherent Dual-Path Diffusion](https://mlsanity.com/paper/2508.05060/) (2025)

## Abstract (arXiv)

Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to sample the space of plausible material parameters, providing a collection of plausible matches that a user can choose from, and efficiently handling both discrete and continuous model parameters. To demonstrate the effectiveness of our framework, we fit procedural models of a range of materials---wall plaster, leather, wood, anisotropic brushed metals and layered metallic paints---to both synthetic and real target images.