Alan Brunton, Can Ates Arikan, Tejas Madan Tanksale, Philipp Urban
ACM Transactions on Graphics (Proc. SIGGRAPH) Volume 37, Issue 4, August 2018
We present an efficient and scalable pipeline for fabricating full-colored objects with spatially-varying translucency from practical and accessible input data via multi-material 3D printing. Observing that the costs associated with BSSRDF measurement and processing are high, the range of 3D printable BSSRDFs are severely limited, and that the human visual system relies only on simple high-level cues to perceive translucency, we propose a method based on reproducing perceptual translucency cues. The input to our pipeline is an RGBA signal defined on the surface of an object, making our approach accessible and practical for designers. We propose a framework for extending standard color management and profiling to combined color and translucency management using a gamut correspondence strategy we call opaque relative processing. We present an efficient streaming method to compute voxel-level material arrangements, achieving both realistic reproduction of measured translucent materials and artistic effects involving multiple fully or partially transparent geometries.
Alan Brunton, Can Ates Arikan, Philipp Urban
ACM Transactions on Graphics (TOG) Volume 35 Issue 1, December 2015
Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
Bui Minh Vu, Philipp Urban, Tejas Madan Tanksale, Shigeki Nakauchi
IS&T Color and Imaging Conference (CIC) 2016, November 2016
Tejas Madan Tanksale, Philipp Urban
IS&T International Symposium on Electronic Imaging 2016 | Measuring, Modeling, and Reproducing Material Appearance 2016, February 2016
Can Ates Arikan, Alan Brunton, Tejas Madan Tanksale, Philipp Urban
Measuring, Modeling, and Reproducing Material Appearance 2015, March 2015
Master's thesis, TU Darmstadt, February 2017
Determining material arrangements to control high-resolution multi-material 3D printers for reproducing shape and visual attributes of a 3D model (e.g. spatially-varying color, translucency and gloss) requires large computational effort. Today's resolution and print tray sizes allow prints with more than 1+e12 voxels each filled with one of the available printing materials (today up to 7 materials can be combined in a single print). Cuttlefish, a 3D printing pipeline, processes the input in a serial fashion leading to increased computation time for higher number of models. Distributed computing is one way of achieving better performance for large computations. Through this master thesis, we have developed a distributed version of the cuttlefish printer driver in which the computational task is distributed amongst multiple nodes in the cluster and the resulting partial output is merged to generate the full slices. The architecture supports streaming, which is required to rapidly start the print before the full computation is finished, as cuttlefish processes the input in small parts and generates chunk-wise output. Finally, the comparison of the performance achieved by the distributed vs the non-distributed cuttlefish version is established to get a better understanding of the advantages and the challenges of distributed computing.
Tejas Madan Tanksale
Master's thesis, TU Darmstadt, July 2015
Colours perceived by humans are influenced by a large number of factors. The same object may look different under different lighting conditions. This is also true for images captured by a camera sensor. In addition to this, each measuring device has its own capturing properties. For example, the RGB intensities captured by different cameras are different for the same object in the same lighting conditions. To avoid these variations in the observed colour, it is necessary to know the ground truth of the colour data of the object, which is given by its spectral reflectance. In this thesis, we devise a method for estimating the spectral reflectances at a fast speed using a tunable monochromatic light source and a trichromatic camera. The estimation is a two-step process: first we need to determine the camera sensitivities, secondly, we use the estimated sensitivities to calculate the reflectances. For both experiments we use the same setup which allows us to use software application programming interfaces (APIs) to obtain reflectances for a large number of targets at an extreme speed and accuracy. For the evaluation of our method, we employ a spectroradiometer which can directly measure the spectra of the targets.