Core Concepts
The authors introduce a hypernetwork model for accurate and generalizable BRDF reconstructions from sparse samples, offering a novel solution for material representation.
Abstract
The paper introduces a technique using hypernetworks to estimate BRDFs accurately from limited samples. The approach allows for reconstruction of unseen materials and compression of densely sampled BRDFs. By leveraging deep learning, the model offers efficient and realistic renderings of complex materials.
The authors highlight the limitations of existing methods in accurately representing complex BRDF functions, emphasizing the need for more adaptable and generalized approaches. They propose a novel framework that combines set encoders, hypernetworks, and neural fields to achieve robust and efficient BRDF reconstructions.
Through experiments on datasets like MERL and RGL, the authors demonstrate the superior performance of their hypernetwork model in terms of accuracy, realism, and compression capabilities. The method outperforms baselines like NBRDF and PCA-based strategies in rendering quality metrics.
Additionally, the paper discusses challenges such as estimating specular components accurately and proposes future directions for improving BRDF editing capabilities through interpretable parameters.
Stats
Our network is trained with 1 458 000 samples per material.
The latent dimension used in training is 7D.
Training the network takes around 15 minutes with an NVIDIA A100 80GB GPU support.
Inference time without GPU support is approximately 0.01 seconds for estimating hyponet weights and around 9 seconds for predicting BRDF values.
The network is trained over 80 epochs.
Quotes
"Our approach offers accurate BRDF reconstructions that are generalizable to new materials."
"The success comes from learning a prior for material appearance through training with multiple materials."