Denis Sumin joined MPI-INF and Saarland University in 2016 as a PhD candidate for computer science, his advisor was Karol Myszkowski.
He received his doctorate from Saarland University.
The abstract of his thesis:
This thesis addresses key challenges in high-fidelity material appearance reproduction for multi-material inkjet 3D printing. Despite recent advances in fullcolor 3D printing, accurate reproduction remains difficult due to the translucency and scattering properties of common print materials. We develop a comprehensive preparation system to overcome these limitations.
Our approach introduces an iterative optimization method to refine volumetric material arrangements, mitigating color bleeding and blurring. Using Monte Carlo light transport simulation and a calibration procedure to obtain material scattering parameters, we achieve superior detail preservation and color fidelity, even in highly translucent media and thin features.
To improve computational efficiency, we propose a deep learning-based approach to predict light scattering in heterogeneous materials. This method accelerates simulations by two orders of magnitude while maintaining high-quality optimization, enabling practical full heterogeneous material optimization within printing times.
Additionally, we evaluate existing image quality metrics for light-field images, develop a dense light-field dataset, and conduct perceptual experiments to assess artifacts in light-field processing. Our findings highlight the need for specialized metrics to better predict perceived quality in complex fabrication tasks. The developed dataset will aid future research in spatially and angularly varying appearance reproduction, contributing to the advancement of 3D printing and computational fabrication.