Neural Acceleration of Scattering‐Aware Color 3D Printing

Rittig, Tobias; Sumin, Denis; Babaei, Vahid; Didyk, Piotr; Voloboy, Alexey; Wilkie, Alex; Bickel, Bernd; Weyrich, Tim; Křivánek, Jaroslav

doi:10.1111/cgf.142626

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…This method is further extended to be capable of reproducing complex geometry [SRB*19]. While MC simulation is expensive, the most recent technique [RSB*21] introduces a deep neural network to predict the scattering within the highly heterogeneous medium, performing around two orders of magnitude faster than MC.…”

Section: Other Related Topicsmentioning

confidence: 99%

“…On the other hand, since MC light transport simulation is expensive in 3D printing fabrication, Rittig et al. [RSB*21] leveraged a deep neural network to predict the surface appearance of scattering within a highly heterogeneous medium. With similar quality levels, it can significantly optimize 3D print preparation for full heterogeneous materials.…”

Section: Other Related Topicsmentioning

confidence: 99%

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State of the Art in Efficient Translucent Material Rendering with BSSRDF

Liang,

Gao,

et al. 2023

Computer Graphics Forum

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Sub‐surface scattering is always an important feature in translucent material rendering. When light travels through optically thick media, its transport within the medium can be approximated using diffusion theory, and is appropriately described by the bidirectional scattering‐surface reflectance distribution function (BSSRDF). BSSRDF methods rely on assumptions about object geometry and light distribution in the medium, which limits their applicability to general participating media problems. However, despite the high computational cost of path tracing, BSSRDF methods are often favoured due to their suitability for real‐time applications. We review these methods and discuss the most recent breakthroughs in this field. We begin by summarizing various BSSRDF models and then implement most of them in a 2D searchlight problem to demonstrate their differences. We focus on acceleration methods using BSSRDF, which we categorize into two primary groups: pre‐computation and texture methods. Then we go through some related topics, including applications and advanced areas where BSSRDF is used, as well as problems that are sometimes important yet are ignored in sub‐surface scattering estimation. In the end of this survey, we point out remaining constraints and challenges, which may motivate future work to facilitate sub‐surface scattering.

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Section: Other Related Topicsmentioning

confidence: 99%

Section: Other Related Topicsmentioning

confidence: 99%

State of the Art in Efficient Translucent Material Rendering with BSSRDF

Liang,

Gao,

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Moreover, because textures on surfaces are often blurred owing to the scattering properties of the material, approaches have been proposed to optimize the internal structure and enable a more detailed representation [17,18]. Furthermore, this technique has been accelerated using neural networks [19]. As another approach, Babaei et al proposed contoning, a method of color representation by which inks are stacked [20].…”

Section: Color Fabricationmentioning

confidence: 99%

Perceptual Translucency in 3D Printing Using Surface Texture

Nagasawa

Ono

Arai³

et al. 2023

J. Imaging

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We propose a method of reproducing perceptual translucency in three-dimensional printing. In contrast to most conventional methods, which reproduce the physical properties of translucency, we focus on the perceptual aspects of translucency. Humans are known to rely on simple cues to perceive translucency, and we develop a method of reproducing these cues using the gradation of surface textures. Textures are designed to reproduce the intensity distribution of the shading and thus provide a cue for the perception of translucency. In creating textures, we adopt computer graphics to develop an image-based optimization method. We validate the effectiveness of the method through subjective evaluation experiments using three-dimensionally printed objects. The results of the validation suggest that the proposed method using texture may increase perceptual translucency under specific conditions. As a method for translucent 3D printing, our method has the limitation that it depends on the observation conditions; however, it provides knowledge to the field of perception that the human visual system can be cheated by only surface textures.

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“…The topic of light transport plays a crucial role in formulating the basis of various domains including traditional computer graphics [13], architecture [14], biomedical imaging [15], nonline-of-sight imaging [16], three-dimensional printing [17], visible light communications [18], holographic recording [19], computational displays [20], eye prescription correction [21], eyegaze tracking [22], ophthalmology [23] and many more. Although we cover only display technologies in this work, an accurate representation method of light transport can potentially pave the way towards enhancements in many other highlighted applications.…”

Section: Optimizing Holograms With Ideal Holographic Light Transportmentioning

confidence: 99%

Learned holographic light transport: invited

Kavaklı

Ürey

Akşit³

2021

Appl. Opt.

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Computer-generated holography algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches.

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Neural Acceleration of Scattering‐Aware Color 3D Printing

Abstract: Figure 1:We propose a neural scattering compensation for 3D color printing. Comparing to a method which uses noise-free Monte Carlo simulation our technique achieves 300× speedup in the above case while providing the same quality.

Cited by 11 publications

References 34 publications

State of the Art in Efficient Translucent Material Rendering with BSSRDF

State of the Art in Efficient Translucent Material Rendering with BSSRDF

Perceptual Translucency in 3D Printing Using Surface Texture

Learned holographic light transport: invited

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