Abstract-Relightable photographs are alternatives to traditional photographs as they provide a richer viewing experience. However, the complex acquisition systems of existing techniques have restricted its usage to specialized setups. We introduce an easy-to-use and affordable solution for using smartphones to acquire the reflectance of paintings and similar almost-planar objects like tablets, engravings and textile. Our goal is to enable interactive relighting of such artifacts by everyone. In our approach, we non-uniformly sample the reflectance functions by moving the LED light of a smartphone and simultaneously tracking the position of the smartphone by using its camera. We then propose a compressive-sensing based approach for reconstructing the light transport matrix from the non-uniformly sampled data. As shown with experiments, we accurately reconstruct the light transport matrix that can then be used to create relightable photographs.Index Terms-Light transport matrix, computational relighting, image based relighting, compressive sensing, non-uniform sampling, mobile imaging.
Stained glass windows are designed to reveal their powerful artistry under diverse and time-varying lighting conditions; virtual relighting of stained glass, therefore, represents an exceptional tool for the appreciation of this age old art form. However, as opposed to most other artifacts, stained glass windows are extremely difficult if not impossible to analyze using controlled illumination because of their size and position. In this paper, we present novel methods built upon image based priors to perform virtual relighting of stained glass artwork by acquiring the actual light transport properties of a given artifact. In a preprocessing step, we build a material-dependent dictionary for light transport by studying the scattering properties of glass samples in a laboratory setup. We can now use the dictionary to recover a light transport matrix in two ways: under controlled illuminations the dictionary constitutes a sparsifying basis for a compressive sensing acquisition, while in the case of uncontrolled illuminations the dictionary is used to perform sparse regularization. The proposed basis preserves volume impurities and we show that the retrieved light transport matrix is heterogeneous, as in the case of real world objects. We present the rendering results of several stained glass artifacts, including the Rose Window of the Cathedral of Lausanne, digitized using the presented methods.
The light transport matrix has become a powerful tool for scene relighting, owing to the versatility of its representational power of various light transport phenomenon. We argue that scenes with an almost planar surface geometry, even with significant amounts of surface roughness, have a banded structure in the light transport matrix. In this paper, we propose a method that exploits this structure of the light transport matrix and provide significant savings in terms of both acquisition time and computation time, while retaining a high accuracy. We validate the proposed algorithm, by recovering the light transport of real objects that exhibit multiple scattering and with rendered scenes exhibiting inter-reflections.
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