Estimating dual-scale properties of glossy surfaces from step-edge lighting

WangChun-Po,; SnavelyNoah,; MarschnerSteve,

doi:10.1145/2070781.2024206

Cited by 24 publications

(5 citation statements)

References 34 publications

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“…We computed the results on a desktop computer with an Intel Xeon 3.70 GHz CPU and a NVIDIA GTX 1080 GPU. The reflectance maps of the planar phones and CD were acquired using second order gradient illumination projected from an LCD panel [6] and the normal map of the holographic paper was captured using Wang et al [20] step edge illumination method. We also compare our renderings to Toisoul&Ghosh [19] prefiltering method in figure 8.…”

Section: Resultsmentioning

confidence: 99%

Real-time rendering of realistic surface diffraction with low rank factorisation

Toisoul

Ghosh

2017

Proceedings of the 14th European Conference on Visual Media Production (CVMP 2017)

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We propose a novel approach for real-time rendering of diffraction effects in surface reflectance in arbitrary environments. Such renderings are usually extremely expensive as they require the computation of a convolution at real-time framerates. In the case of diffraction, the diffraction lobes usually have high frequency details that can only be captured with high resolution convolution kernels which make calculations even more expensive. Our method uses a low rank factorisation of the diffraction lookup table to approximate a 2D convolution kernel by two simpler low rank kernels which allow the computation of the convolution at real-time framerates using two rendering passes. We show realistic renderings in arbitrary environments and achieve a performance from 50 to 100 FPS making possible to use such a technique in real-time applications such as video games and VR.

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Section: Resultsmentioning

confidence: 99%

Real-time rendering of realistic surface diffraction with low rank factorisation

Toisoul

Ghosh

2017

Proceedings of the 14th European Conference on Visual Media Production (CVMP 2017)

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“…Lightweight capture methods trade accuracy for simplicity to enable SVBRDF capture with as few as a single photograph of a surface – typically planar. Such methods compensate for the measurement scarcity by making various assumptions on the materials to be acquired, such as the existence of a low‐dimensional basis of BRDFs [DWT*10; RWS*11; HSL*17; ZCD*16], or the presence of repetitive or stochastic patterns [AWL*15; WSM11].…”

Section: Background and Related Workmentioning

confidence: 99%

Single‐Image SVBRDF Estimation with Learned Gradient Descent

Luo,

Scandolo,

Bousseau

et al. 2024

Computer Graphics Forum

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Recovering spatially‐varying materials from a single photograph of a surface is inherently ill‐posed, making the direct application of a gradient descent on the reflectance parameters prone to poor minima. Recent methods leverage deep learning either by directly regressing reflectance parameters using feed‐forward neural networks or by learning a latent space of SVBRDFs using encoder‐decoder or generative adversarial networks followed by a gradient‐based optimization in latent space. The former is fast but does not account for the likelihood of the prediction, i.e., how well the resulting reflectance explains the input image. The latter provides a strong prior on the space of spatially‐varying materials, but this prior can hinder the reconstruction of images that are too different from the training data. Our method combines the strengths of both approaches. We optimize reflectance parameters to best reconstruct the input image using a recurrent neural network, which iteratively predicts how to update the reflectance parameters given the gradient of the reconstruction likelihood. By combining a learned prior with a likelihood measure, our approach provides a maximum a posteriori estimate of the SVBRDF. Our evaluation shows that this learned gradient‐descent method achieves state‐of‐the‐art performance for SVBRDF estimation on synthetic and real images.

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“…18d. Here, we employed the bump map of a holographic paper acquired with step-edge illumination [Wang et al 2011]. Fig.…”

Section: Polarization Imagingmentioning

confidence: 99%

Acquiring spatially varying appearance of printed holographic surfaces

2018

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We present two novel and complimentary approaches to measure diffraction effects in commonly found planar spatially varying holographic surfaces. Such surfaces are increasingly found in various decorative materials such as gift bags, holographic papers, clothing and security holograms, and produce impressive visual effects that have not been previously acquired for realistic rendering. Such holographic surfaces are usually manufactured with one dimensional diffraction gratings that are varying in periodicity and orientation over an entire sample in order to produce a wide range of diffraction effects such as gradients and kinematic (rotational) effects. Our proposed methods estimate these two parameters and allow an accurate reproduction of these effects in real-time. The first method simply uses a point light source to recover both the grating periodicity and orientation in the case of regular and stochastic textures. Under the assumption that the sample is made of the same repeated diffractive tile, good results can be obtained using just one to five photographs on a wide range of samples. The second method is based on polarization imaging and enables an independent high resolution measurement of the grating orientation and relative periodicity at each surface point. The method requires a minimum of four photographs for accurate results, does not assume repetition of an exemplar tile, and can even reveal minor fabrication defects. We present point light source renderings with both approaches that qualitatively match photographs, as well as real-time renderings under complex environmental illumination.

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Estimating dual-scale properties of glossy surfaces from step-edge lighting

Cited by 24 publications

References 34 publications

Real-time rendering of realistic surface diffraction with low rank factorisation

Real-time rendering of realistic surface diffraction with low rank factorisation

Single‐Image SVBRDF Estimation with Learned Gradient Descent

Acquiring spatially varying appearance of printed holographic surfaces

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