Learning Sparse BRDF Measurement Samples from Image

Problem
This paper addresses the challenge of efficient Bidirectional Reflectance Distribution Function (BRDF) measurement acquisition, which is critical for realistic rendering in computer graphics. Traditional methods rely on dense gonioreflectometer measurements, which are both time-consuming and costly. The authors propose a novel approach to select a minimal set of BRDF measurements that maximize the reconstruction quality of material appearance, leveraging a learned reflectance prior. This work is presented as a preprint and has not yet undergone peer review.

Method
The proposed method integrates a set encoder designed for sparse coordinate-value observations with a pretrained hypernetwork-based BRDF reconstructor and a differentiable renderer. The training process involves fixing the reconstructor while optimizing the measurement locations based on gradients derived from both BRDF-space and rendered-image losses. This decoupling allows the sampler to focus on selecting measurement directions that are most informative given the learned material distribution. The architecture employs a hypernetwork to generalize across different materials, and the differentiable renderer facilitates end-to-end optimization. The training compute specifics are not disclosed, but the method emphasizes the efficiency of using fewer measurements (8 and 16) compared to traditional dense sampling.

Results
Experiments conducted on the MERL dataset demonstrate that the proposed sampler significantly enhances reconstruction quality at low measurement budgets (8 and 16 samples) when compared to neural reconstruction baselines. Specifically, the method achieves a notable improvement in rendering fidelity, outperforming existing techniques in low-budget scenarios. While PCA-based methods maintain competitive performance at larger measurement budgets, the proposed approach excels in scenarios where measurement resources are limited, indicating a clear advantage in practical applications.

Limitations
The authors acknowledge several limitations, including the reliance on a pretrained hypernetwork, which may not generalize well to all material types, particularly those not represented in the training set. Additionally, the method’s performance at larger measurement budgets is not as strong compared to PCA-based approaches, suggesting that the benefits of the proposed sampler are most pronounced at lower budgets. The paper does not address potential computational overhead introduced by the differentiable rendering process, which could impact real-time applications.

Why it matters
This work has significant implications for the fields of computer graphics and material science, particularly in applications requiring efficient BRDF measurement techniques. By enabling high-quality material reconstruction with fewer measurements, the proposed method can reduce costs and time associated with BRDF acquisition, making realistic rendering more accessible. Furthermore, the insights gained from the analysis of image-space supervision and co-optimization could inform future research on material representation and rendering techniques, potentially leading to advancements in machine learning applications within graphics.

Authors: Wen Cao
Source: arXiv:2604.26740
URL: https://arxiv.org/abs/2604.26740v1