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

像绘画这样的精美艺术品是我们社会和文化遗产的重要组成部分，但本质上是容易退化或损坏的。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和学习它们，这是至关重要的。幸运的是，这些文化瑰宝的高质量复制品可以在许多应用中代替原件，包括修复实践、音乐学院研究、博物馆教育和家庭娱乐。该项目将探索如何利用新兴一代的高质量3D打印机，通过使用少量墨水（与消费级打印机一样），而是多达10个墨水，来生产具有前所未有质量的精美艺术画作的复制品。事件油墨重现的颜色，使他们是无法区分的原始的人眼在各种照明条件下。此外，使用这些现代的多材料3D打印机不仅可以再现色彩，还可以再现光泽、半透明和小尺度的细节，如笔触。通过创建&amp；#64257；RST完整的流水线逼真再现&amp；#64257；新艺术（以绘画为重点），这个项目将为如何一般来说，新艺术复制应该使用计算过程和3D打印机来完成。这将导致能够复制和保存文化遗产的技术，使广大人民能够以与原件无法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，将有助于与策展人和公众建立强有力的联系。项目成果将产生额外的广泛影响，通过3D打印实现精确的外观再现，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摄像系统来捕捉原画的属性。这涉及到测量全光谱的光。通过画上的每个点，以及表面的光泽或不透明程度来检测。此外，它还涉及到使用高分辨率3D扫描仪来测量没有刷子或其他工具在表面留下的凸起。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，并将测量的油墨特性结合到计算机模拟中，以准确预测打印输出的外观。, # 64257;该项目的另一个组成部分是执行计算机优化，以确定应该使用的印刷材料的确切类型，数量和浓度，以及如何调整印刷过程以达到最大的复制质量。该奖项反映了美国国家科学基金会的法定使命，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.