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Neural light field 3D printing
ACM Transactions on Graphics  ( IF 6.2 ) Pub Date : 2020-11-27 , DOI: 10.1145/3414685.3417879
Quan Zheng 1 , Vahid Babaei 1 , Gordon Wetzstein 2 , Hans-Peter Seidel 1 , Matthias Zwicker 3 , Gurprit Singh 1
Affiliation  

Modern 3D printers are capable of printing large-size light-field displays at high-resolutions. However, optimizing such displays in full 3D volume for a given light-field imagery is still a challenging task. Existing light field displays optimize over relatively small resolutions using a few co-planar layers in a 2.5D fashion to keep the problem tractable. In this paper, we propose a novel end-to-end optimization approach that encodes input light field imagery as a continuous-space implicit representation in a neural network. This allows fabricating high-resolution, attenuation-based volumetric displays that exhibit the target light fields. In addition, we incorporate the physical constraints of the material to the optimization such that the result can be printed in practice. Our simulation experiments demonstrate that our approach brings significant visual quality improvement compared to the multilayer and uniform grid-based approaches. We validate our simulations with fabricated prototypes and demonstrate that our pipeline is flexible enough to allow fabrications of both planar and non-planar displays.

中文翻译:

神经光场3D打印

现代 3D 打印机能够以高分辨率打印大尺寸的光场显示器。然而,针对给定的光场图像以全 3D 体积优化此类显示仍然是一项具有挑战性的任务。现有的光场显示器以 2.5D 方式使用一些共面层在相对较小的分辨率上进行优化,以保持问题的可控性。在本文中,我们提出了一种新颖的端到端优化方法,将输入光场图像编码为神经网络中的连续空间隐式表示。这允许制造呈现目标光场的高分辨率、基于衰减的体积显示器。此外,我们将材料的物理约束结合到优化中,以便可以在实践中打印结果。我们的模拟实验表明,与基于多层和统一网格的方法相比,我们的方法带来了显着的视觉质量改进。我们用制造的原型验证了我们的模拟,并证明我们的管道足够灵活,可以制造平面和非平面显示器。
更新日期:2020-11-27
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