Search Results (2)

The application of materials with changing visual properties with lighting and observation directions has found broad utility across diverse industries, from architecture and fashion to automotive and film production. The expanding array of applications and appearance reproduction requirements emphasizes the critical role of material appearance measurement and surface characterization. Such measurements offer twofold benefits in soft proofing and product quality control, reducing errors and material waste while providing objective quality assessment. Some image-based setups have been proposed to capture the appearance of material surfaces with spatial variations in visual properties in terms of Spatially Varying Bidirectional Reflectance Distribution Functions (SVBRDF) and Bidirectional Texture Functions (BTF). However, comprehensive exploration of optical design concerning spectral channels and per-pixel incident-reflection direction calculations, along with measurement validation, remains an unexplored domain within these systems. Therefore, we developed a novel advanced multispectral image-based device designed to measure SVBRDF and BTF, addressing these gaps in the existing literature. Central to this device is a novel rotation table as sample holder and passive multispectral imaging. In this paper, we present our compact multispectral image-based appearance measurement device, detailing its design, assembly, and optical considerations. Preliminary measurements showcase the device’s potential in capturing angular and spectral data, promising valuable insights into material appearance properties. Full article

Photo-realistic representation in user-specified view and lighting conditions is a challenging but high-demand technology in the digital transformation of cultural heritages. Despite recent advances in neural renderings, it is still necessary to capture high-quality surface reflectance from photography in a controlled environment for real-time applications such as VR/AR and digital arts. In this paper, we present a deep embedding clustering network for spatially-varying bidirectional reflectance distribution function (SVBRDF) estimation. Our network is designed to simultaneously update the reflectance basis and its linear manifold in the spatial domain of SVBRDF. We show that our dual update scheme excels in optimizing the rendering loss in terms of the convergence speed and visual quality compared to the current iterative SVBRDF update methods. Full article