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Computer Science > Graphics

arXiv:2410.02917 (cs)
[Submitted on 3 Oct 2024]

Title:Deep image-based Adaptive BRDF Measure

Authors:Wen Cao
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Abstract:Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in high quality image rendering and physically accurate sensor simulation. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. Taking an image of the physical material sample as input a lightweight neural network first estimates the parameters of an analytic BRDF model, and the distribution of the sample locations. In a second step we use an image based loss to find the number of samples required to meet the accuracy required. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representation.
Comments: 9
Subjects: Graphics (cs.GR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2410.02917 [cs.GR]
  (or arXiv:2410.02917v1 [cs.GR] for this version)
  https://doi.org/10.48550/arXiv.2410.02917

Submission history

From: Wen Cao [view email]
[v1] Thu, 3 Oct 2024 19:08:48 UTC (34,005 KB)
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