Deep image-based Adaptive BRDF Measure

TL;DR

This paper introduces a neural network-based method that efficiently estimates BRDF parameters from images, reducing sampling requirements and accelerating the measurement process while maintaining high accuracy.

Contribution

A novel approach combining neural networks and image-based loss to minimize samples needed for accurate BRDF measurement.

Findings

Significantly reduces measurement time for BRDF capture.

Maintains high fidelity in BRDF representation with fewer samples.

Accelerates the process without sacrificing accuracy.

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.