39 research outputs found
Material acquisition using deep learning
International audienceTexture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in pictures. Designing algorithms able to leverage these cues to recover spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a few images has challenged computer graphics researchers for decades. I explore the use of deep learning to tackle lightweight appearance capture and make sense of these visual cues. Our networks are capable of recovering per-pixel normals, diffuse albedo, specular albedo and specular roughness from as little as one picture of a flat surface lit by a hand-held flash. We propose a method which improves its prediction with the number of input pictures, and reaches high quality reconstructions with up to 10 images -- a sweet spot between existing single-image and complex multi-image approaches. We introduce several innovations on training data acquisition and network design, bringing clear improvement over the state of the art for lightweight material capture
Mejora de un modelo de representación de materiales para su uso en edición intuitiva de apariencia
Durante los últimos años se ha producido un gran avance en las técnicas de captura de materiales. Sin embargo, editar de forma intuitiva materiales sigue siendo un reto. Este trabajo se basa en un método existente y consigue ampliar el rango de apariencia que se puede obtener durante la edición
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR
Based on powerful text-to-image diffusion models, text-to-3D generation has
made significant progress in generating compelling geometry and appearance.
However, existing methods still struggle to recover high-fidelity object
materials, either only considering Lambertian reflectance, or failing to
disentangle BRDF materials from the environment lights. In this work, we
propose Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR
(\textbf{MATLABER}) that leverages a novel latent BRDF auto-encoder for
material generation. We train this auto-encoder with large-scale real-world
BRDF collections and ensure the smoothness of its latent space, which
implicitly acts as a natural distribution of materials. During appearance
modeling in text-to-3D generation, the latent BRDF embeddings, rather than BRDF
parameters, are predicted via a material network. Through exhaustive
experiments, our approach demonstrates the superiority over existing ones in
generating realistic and coherent object materials. Moreover, high-quality
materials naturally enable multiple downstream tasks such as relighting and
material editing. Code and model will be publicly available at
\url{https://sheldontsui.github.io/projects/Matlaber}
Material acquisition using deep learning
International audienceTexture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in pictures. Designing algorithms able to leverage these cues to recover spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a few images has challenged computer graphics researchers for decades. I explore the use of deep learning to tackle lightweight appearance capture and make sense of these visual cues. Our networks are capable of recovering per-pixel normals, diffuse albedo, specular albedo and specular roughness from as little as one picture of a flat surface lit by a hand-held flash. We propose a method which improves its prediction with the number of input pictures, and reaches high quality reconstructions with up to 10 images -- a sweet spot between existing single-image and complex multi-image approaches. We introduce several innovations on training data acquisition and network design, bringing clear improvement over the state of the art for lightweight material capture
An intuitive control space for material appearance
Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction