# Uncertainty for SVBRDF Acquisition using Frequency Analysis

arXiv: https://arxiv.org/abs/2406.17774 · HTML: https://mlsanity.com/paper/2406.17774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Extracting Triangular 3D Models, Materials, and Lighting From Images](https://mlsanity.com/paper/2111.12503/) (2021)
- [Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising](https://mlsanity.com/paper/2206.03380/) (2022)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [Two-shot Spatially-varying BRDF and Shape Estimation](https://mlsanity.com/paper/2004.00403/) (2020)
- [Deep Polarization Imaging for 3D shape and SVBRDF Acquisition](https://mlsanity.com/paper/2105.02875/) (2021)
- [Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks](https://mlsanity.com/paper/1809.00886/) (2018)
- [Flexible SVBRDF Capture with a Multi-Image Deep Network](https://mlsanity.com/paper/1906.11557/) (2019)
- [ControlMat: A Controlled Generative Approach to Material Capture](https://mlsanity.com/paper/2309.01700/) (2023)
- [Neural-PBIR Reconstruction of Shape, Material, and Illumination](https://mlsanity.com/paper/2304.13445/) (2023)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [Guided Fine-Tuning for Large-Scale Material Transfer](https://mlsanity.com/paper/2007.03059/) (2020)
- [Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory](https://mlsanity.com/paper/1809.01293/) (2018)
- [UMat: Uncertainty-Aware Single Image High Resolution Material Capture](https://mlsanity.com/paper/2305.16312/) (2023)
- [Sparse Ellipsometry: Portable Acquisition of Polarimetric SVBRDF and Shape with Unstructured Flash Photography](https://mlsanity.com/paper/2207.04236/) (2022)
- [Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering](https://mlsanity.com/paper/2310.00362/) (2023)
- [JoIN: Joint GANs Inversion for Intrinsic Image Decomposition](https://mlsanity.com/paper/2305.11321/) (2023)

## Built on by (most cited first, 4 of 4)

- [PAM: A Pose-Appearance-Motion Engine for Sim-to-Real HOI Video Generation](https://mlsanity.com/paper/2603.22193/) (2026)
- [Light Field Based 6DoF Tracking of Previously Unobserved Objects](https://mlsanity.com/paper/2512.13007/) (2025)
- [Heat Kernel Textures: the Geodesic Gaussians That Do Not Splat](https://mlsanity.com/paper/2609.07557/) (2026)
- [Diffusion-Based Material Regularization for Physics-Based Inverse Rendering](https://mlsanity.com/paper/2606.31065/) (2026)

## Abstract (arXiv)

This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the observations contain enough information to reconstruct the appearance properties of a captured object. We study this ambiguity, or uncertainty, using entropy and accelerate the analysis by using the frequency domain, rather than the domain of incoming and outgoing viewing angles. The result is a method that computes a map of uncertainty over an entire object within a millisecond. We find that the frequency model allows us to recover SVBRDF parameters with competitive performance, that the accelerated entropy computation matches results with a physically-based path tracer, and that there is a positive correlation between error and uncertainty. We then show that the uncertainty map can be applied to improve SVBRDF acquisition using capture guidance, sharing information on the surface, and using a diffusion model to inpaint uncertain regions. Our code is available at https://github.com/rubenwiersma/svbrdf_uncertainty.