Skip to main content

Deep multispectral painting reproduction via multi-layer, custom-ink printing

Author(s): Shi, Liang; Babaei, Vahid; Kim, Changil; Foshey, Michael; Hu, Yuanming; et al

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr13g1g
Abstract: We propose a workflow for spectral reproduction of paintings, which captures a painting's spectral color, invariant to illumination, and reproduces it using multi-material 3D printing. We take advantage of the current 3D printers' capabilities of combining highly concentrated inks with a large number of layers, to expand the spectral gamut of a set of inks. We use a data-driven method to both predict the spectrum of a printed ink stack and optimize for the stack layout that best matches a target spectrum. This bidirectional mapping is modeled using a pair of neural networks, which are optimized through a problem-specific multi-objective loss function. Our loss function helps find the best possible ink layout resulting in the balance between spectral reproduction and colorimetric accuracy under a multitude of illuminants. In addition, we introduce a novel spectral vector error diffusion algorithm based on combining color contoning and halftoning, which simultaneously solves the layout discretization and color quantization problems, accurately and efficiently. Our workflow outperforms the state-of-the-art models for spectral prediction and layout optimization. We demonstrate reproduction of a number of real paintings and historically important pigments using our prototype implementation that uses 10 custom inks with varying spectra and a resin-based 3D printer.
Publication Date: Dec-2018
Citation: Shi, Liang, Vahid Babaei, Changil Kim, Michael Foshey, Yuanming Hu, Pitchaya Sitthi-Amorn, Szymon Rusinkiewicz, and Wojciech Matusik. "Deep multispectral painting reproduction via multi-layer, custom-ink printing." ACM Transactions on Graphics 37, no. 6 (2018): 271:1-271:15. doi:10.1145/3272127.3275057
DOI: 10.1145/3272127.3275057
ISSN: 0730-0301
EISSN: 1557-7368
Pages: 271:1 - 271:15
Type of Material: Journal Article
Journal/Proceeding Title: ACM Transactions on Graphics
Version: Author's manuscript



Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.