Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in realistic image rendering. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel iterative method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. The method is a two-step approach, where the first step takes an image of the physical material as input and uses a lightweight neural network to estimate the parameters of an analytic BRDF model. The second step adaptive sample the measurements using the estimated BRDF model and an image loss to maximize the BRDF representation accuracy. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representation.

Hand in hand with the rapid development of machine learning, deep learning and generative AI algorithms and architectures, the graphics community has seen a remarkable evolution of novel techniques for material and appearance capture. Typically, these machine-learning-driven methods and technologies, in contrast to traditional techniques, rely on only a single or very few input images, while enabling the recovery of detailed, high-quality measurements of bi-directional reflectance distribution functions, as well as the corresponding spatially varying material properties, also known as Spatially Varying Bi-directional Reflectance Distribution Functions (SVBRDFs). Learning-based approaches for appearance capture will play a key role in the development of new technologies that will exhibit a significant impact on virtually all domains of graphics. Therefore, to facilitate future research, this State-of-the-Art Report (STAR) presents an in-depth overview of the state-of-the-art in machine-learning-driven material capture in general, and focuses on SVBRDF acquisition in particular, due to its importance in accurately modelling complex light interaction properties of real-world materials. The overview includes a categorization of current methods along with a summary of each technique, an evaluation of their functionalities, their complexity in terms of acquisition requirements, computational aspects and usability constraints. The STAR is concluded by looking forward and summarizing open challenges in research and development toward predictive and general appearance capture in this field. A complete list of the methods and papers reviewed in this survey is available at . Papers surveyed in this study with a focus on the extraction of BRDF or SVBRDF from a few measurements, classifying them according to their specific geometries and lighting conditions. Whole-scene refers to techniques that capture entire indoor or outdoor outside the scope of this survey. image

This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectral-coded mask and a microlens array to capture spatial, angular, and spectral information using a singlemonochrome sensor. We propose a model that employs compressed sensing techniques to reconstruct thecomplete multi-spectral light field from undersampled measurements. Unlike previous work where a lightfield is vectorized to a 1D signal, our method employs a 5D basis and a novel 5D measurement model, hence,matching the intrinsic dimensionality of multispectral light fields. We mathematically and empirically showthe equivalence of 5D and 1D sensing models, and most importantly that the 5D framework achieves or-ders of magnitude faster reconstruction while requiring a small fraction of the memory. Moreover, our newmultidimensional sensing model opens new research directions for designing efficient visual data acquisitionalgorithms and hardware.