CHS: Small: Collaborative Research: 3D Printing for High Fidelity Image Reproduction Capturing Texture, Spectral Color, Gloss, and Translucency

绘画等艺术品是我们社会和文化遗产的重要组成部分，但本质上容易退化或损坏。保护这些珍贵的作品需要巨大的成本，但如果后代有机会欣赏它们并从中学习，这是至关重要的。幸运的是，这些文化宝藏的高质量复制品可以在许多应用中取代原件，包括修复实践，温室研究，博物馆教育和家庭享受。该项目将探索如何利用新一代高质量3D打印机，不仅使用少量墨水（与消费级打印机一样），而且使用多达10种不同的墨水来复制颜色，从而在各种照明条件下无法区分原始颜色，从而生产出前所未有的质量的美术画复制品。此外，使用这些现代多材料3D打印机不仅可以再现颜色，还可以再现光泽，透明度和新尺度细节，如笔触。通过创建#64257;第一个完整的流水线，用于#64257;新艺术的逼真再现（重点是绘画），该项目将为如何使用计算过程和3D打印机进行#64257;新艺术再现奠定基础。这将促成复制和保护文化遗产的技术，使广大民众能够以与原作难以区分的形式欣赏喜爱的绘画。与波士顿和普林斯顿当地博物馆的合作将使策展人和公众能够广泛接触。项目成果将产生更广泛的影响，通过3D打印实现精确的外观复制，支持快速原型和最终用户产品制造的广泛应用。为了实现这些目标，该项目将研究几种关键方法和技术。首先，有一个问题是如何使用先进的相机系统来捕捉原始绘画的属性。这包括测量绘画上每个点反射的全光谱，以及表面的光泽或不透明程度。此外，它涉及使用高分辨率3D扫描仪来测量刷子或其他工具在表面上留下的#64257;新凸起。该项目的第二个组成部分是表征多材料3D打印机的全部功能，并将测量的墨水特性纳入计算机模拟中，以准确预测打印输出的外观。#64257&;该项目的最终组成部分是进行计算机优化，以确定应使用的打印材料的确切类型，数量和浓度，以及如何调整打印过程以达到最高的复制质量。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Fine art objects such as paintings form a key part of our social and cultural heritage, but are inherently subject to degradation or damage. Conservation of these precious works incurs a huge cost, yet is vital if future generations are to have an opportunity to enjoy them and learn from them. Fortunately, high quality facsimiles of these cultural treasures can be exploited in place of the originals in many applications, including restoration practice, conservatory studies, education in museums, and enjoyment at home. This project will explore how to exploit an emerging generation of high-quality 3D printers to produce reproductions of fine art paintings that are of unprecedented quality by using not just a small number of inks (as with consumer-grade printers) but as many as 10 different inks to reproduce colors such that they are indistinguishable from the original to the human eye under various lighting conditions. Moreover, the use of these modern multi-material 3D printers will enable not just the reproduction of color but also the recreation of gloss, translucency, and fine-scale detail such as brush strokes. By creating the first complete pipeline for realistic reproduction of fine art (with a focus on paintings), this project will lay the foundations for how fine art reproduction, in general, should be done using computational processes and 3D printers. This will lead to enabling technologies for reproduction and preservation of cultural heritage, allowing a wide population to enjoy favorite paintings in a form that is indistinguishable from the originals. Collaboration with local museums in Boston and Princeton will enable strong outreach with curators and the general public. Project outcomes will have additional broad impact by enabling accurate appearance reproduction with 3D printing in support of a wide range of applications in rapid prototyping and the manufacture of end-user products.To achieve these goals, the project will investigate several key methods and technologies. First, there is the question of how to use advanced camera systems to capture the properties of the original paintings. This involves measuring the full spectrum of light reflected by each point on the painting, as well as how shiny or opaque the surface is. Moreover, it involves the use of high-resolution 3D scanners to measure the fine bumps on the surface left by brushes or other tools. A second component of the project is to characterize the full capabilities of multi-material 3D printers, and to incorporate the measured ink properties into computer simulations that can predict exactly how the print-outs will look. The final components of the project are to perform computer optimization to determine the exact type, number, and concentration of printing materials that should be used, and how the printing process should be tweaked to achieve maximum reproduction quality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.