Neural Reflectance Capture in the View-Illumination Domain

Kang, Kaizhang; Gu, Minyi; Xie, Cihui; Yang, Xuanda; Wu, Hongzhi; Zhou, Kun

doi:10.1109/tvcg.2021.3117370

Cited by 5 publications

(1 citation statement)

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“…Reflectance capture for opaque objects have been well researched in computer graphics. Various capture setups with controlled illumination have been proposed for measuring the shape and reflectance of an opaque object [MHP*07; GFT*11; KXH*19; KGX*23]. Most lightstage setups utilize discrete lighting to approximate the illumination.…”

Section: Related Workmentioning

confidence: 99%

Practical Acquisition of Shape and Plausible Appearance of Reflective and Translucent Objects

Lin

Lin²,

Ghosh

2023

Computer Graphics Forum

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We present a practical method for acquisition of shape and plausible appearance of reflective and translucent objects for realistic rendering and relighting applications. Such objects are extremely challenging to scan with existing capture setups, and have previously required complex lightstage hardware emitting continuous illumination. We instead employ a practical capture setup consisting of a set of desktop LCD screens to illuminate such objects with piece‐wise continuous illumination for acquisition. We employ phase‐shifted sinusoidal illumination for novel estimation of high quality photometric normals and transmission vector along with diffuse‐specular separated reflectance/transmission maps for realistic relighting. We further employ neural in‐painting to fill gaps in our measurements caused by gaps in screen illumination, and a novel NeuS‐based neural rendering that combines these shape and reflectance maps acquired from multiple viewpoints for high‐quality 3D surface geometry reconstruction along with plausible realistic rendering of complex light transport in such objects.

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Section: Related Workmentioning

confidence: 99%