Appearance

We predict the appearance of materials from their physical microstructure

The appearance of a material is largely determined by the microstructure of its surface and the particles beneath its surface. We construct mathematical models for predicting material appearance based on surface properties and particle content. We also investigate the challenges of fast and accurate rendering of material appearance. The goal is to interactively compute realistic images of real-world materials.

About the project

Modeling and rendering of material appearance is highly challenging due to the nontrivial physical composition of most materials and due to the complex nature of light. We need mathematical models at a scale macroscopic enough to describe light-material interaction useful for graphical rendering of a scene, but we also need the complexity to be high enough to produce realistic images. This project is all about striking the right balance.

Contact

Morten Hannemose
[Post Doc]
Technical University of Denmark
Jeppe Revall Frisvad
[Associate Professor]
Technical University of Denmark

Publications

Alignment of rendered images with photographs for testing appearance models [2020]

M. Hannemose, M. E. B. Doest, A. Luongo, S. K. S. Gregersen, J. Wilm, J. R. Frisvad
Applied Optics , 59(31), 9786-9798

Computing the bidirectional scattering of a microstructure using scalar diffraction theory and path tracing [2020]

V. Falster, A. Jarabo, J. R. Frisvad
Computer Graphics Forum (PG 2020) , 39(7), 231-242

Survey of models for acquiring the optical properties of translucent materials [2020]

J. R. Frisvad, S. A. Jensen, J. S. Madsen, A. Correia, L. Yang, S. K. S. Gregersen, Y. Meuret, P. Hansen
Computer Graphics Forum (EG 2020) , 39(2), 729-755

Predicting and 3D Printing Material Appearance [2019]

A. Luongo
Department of Applied Mathematics and Computer Science, Technical University of Denmark

Towards Interactive Photorealistic Rendering [2018]

A. Dal_Corso
Department of Applied Mathematics and Computer Science, Technical University of Denmark

Modeling the Anisotropic Reflectance of a Surface with Microstructure Engineered to Obtain Visible Contrast after Rotation [2017]

A. Luongo, V. Falster, M. B. Doest, D. Li, F. Regi, Y. Zhang, G. Tosello, J. B. Nielsen, H. Aanæs, J. R. Frisvad
Proceedings of International Conference on Computer Vision Workshop (ICCVW 2017) , IEEE, 159-165

Scene reassembly after multimodal digitization and pipeline evaluation using photorealistic rendering [2017]

J. D. Stets, A. Dal_Corso, J. B. Nielsen, R. A. Lyngby, S. H. N. Jensen, J. Wilm, M. B. Doest, C. Gundlach, E. R. Eiriksson, K. Conradsen, A. B. Dahl, J. A. Bærentzen, J. R. Frisvad, H. Aanæs
Applied Optics , 56(27), 7679-7690

Interactive Appearance Prediction for Cloudy Beverages [2016]

A. Dal_Corso, J. R. Frisvad, T. K. Kjeldsen, J. A. Bærentzen
Workshop on Material Appearance Modeling (MAM 2016) , The Eurographics Association, 1-4

Hybrid fur rendering: combining volumetric fur with explicit hair strands [2016]

T. G. Andersen, V. Falster, J. R. Frisvad, N. J. Christensen
The Visual Computer , 32(6), 739-749

Interactive directional subsurface scattering and transport of emergent light [2016]

A. Dal_Corso, J. R. Frisvad, J. Mosegaard, J. A. Bærentzen
The Visual Computer , 33(3), 371-383

Quality Assurance Based on Descriptive and Parsimonious Appearance Models [2015]

J. B. Nielsen, E. R. Eiriksson, R. L. Kristensen, J. Wilm, J. R. Frisvad, K. Conradsen, H. Aanæs
Workshop on Material Appearance Modeling , The Eurographics Association, 21-24

Directional Dipole Model for Subsurface Scattering [2014]

J. R. Frisvad, T. Hachisuka, T. K. Kjeldsen
ACM Transactions on Graphics , 34(1), 5:1-5:12

Real-Time Rendering of Teeth with No Preprocessing [2012]

C. T. Larsen, J. R. Frisvad, P. D. E. Jensen, J. A. Bærentzen
Advances in Visual Computing (Proceedings of ISVC 2012) , Springer, 7432, 334-345

Predicting the Appearance of Materials Using Lorenz-Mie Theory [2012]

J. R. Frisvad, N. J. Christensen, H. W. Jensen
The Mie Theory: Basics and Applications , 169, 101-133

Light, Matter, and Geoemtry: The Cornerstones of Appearance Modelling [2008]

J. R. Frisvad
Department of Informatics and Mathematical Modelling, Technical University of Denmark

Computing the Scattering Properties of Participating Media Using Lorenz-Mie Theory [2007]

J. R. Frisvad, N. J. Christensen, H. W. Jensen
ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2007) , 26(3), 60:1-60:10

Alignment of rendered images with photographs for testing appearance models


Abstract

We propose a method for direct comparison of rendered images with a corresponding photograph in order to analyze the optical properties of physical objects and test the appropriateness of appearance models. To this end, we provide a practical method for aligning a known object and a point-like light source with the configuration observed in a photograph. Our method is based on projective transformation of object edges and silhouette matching in the image plane. To improve the similarity between rendered and photographed objects, we introduce models for spatially varying roughness and a model where the distribution of light transmitted by a rough surface influences direction-dependent subsurface scattering. Our goal is to support development toward progressive refinement of appearance models through quantitative validation.

Dataset

The dataset can be used to evaluate rendering techniques or for inverse rendering. It consists of three different objects, from multiple different viewpoints with different light positions. Each image has a segmentation and the estimated camera pose and light source position are stored in a json file. This is accompanied by a triangle mesh of the object. Please cite if you use the data.

Dataset

The image of the bunny along with the segmentation.

Dataset

Images of the angel, shown with segmentations. Two camera poses and one light source position.

Dataset

Images of the bust taken from four different camera positions, each with five different light source positions. Segmentations are also available.

BibTex Reference

@article{hannemose2020alignment,
  title =   {Alignment of rendered images with photographs for testing appearance models},
  author =  {Morten Hannemose and Mads Emil Brix Doest and Andrea Luongo and S{\o}ren Kimmer Schou Gregersen and Jakob Wilm and Jeppe Revall Frisvad},
  journal = {Applied Optics},
  year =    {2020},
  month =   {October},
  url =     {https://people.compute.dtu.dk/jerf/papers/alignment_lowres.pdf},
}

Download Dataset