Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future.

This study investigates the potential impact of subsurface light transport on gloss perception for the purposes of broadening our understanding of visual appearance in computer graphics applications. Gloss is an important attribute for characterizing material appearance. We hypothesize that subsurface scattering of light impacts the glossiness perception. However, gloss has been traditionally studied as a surface-related quality and the findings in the state-of-the-art are usually based on fully opaque materials, although the visual cues of glossiness can be impacted by light transmission as well. To address this gap and to test our hypothesis, we conducted psychophysical experiments and found that subjects are able to tell the difference in terms of gloss between stimuli that differ in subsurface light transport but have identical surface qualities and object shape. This gives us a clear indication that subsurface light transport contributes to a glossy appearance. Furthermore, we conducted additional experiments and found that the contribution of subsurface scattering to gloss varies across different shapes and levels of surface roughness. We argue that future research on gloss should include transparent and translucent media and to extend the perceptual models currently limited to surface scattering to more general ones inclusive of subsurface light transport.

Archaeological artifacts play important role in understanding the past developments of the humanity. However, the artifacts are often highly fragmented and degraded, with many details and parts missing due to centuries’ long degradation. Archaeologists and conservators attempt to reconstruct the original state of the objects either physically or virtually. This process includes characterizing and matching fragments’ features to identify which ones belong together. However, this process currently requires an extensive and tedious manual labor. Recent development in computational techniques gave rise to computer-assisted ways of virtual reconstruction, where the computer suggests solutions to the puzzle of scattered fragments and supplements or fully replaces manual labor. However, the capabilities of computational techniques remain limited in many aspects. This review summarizes the state-of-the-art computational techniques for puzzle and virtual reconstruction problems in cultural heritage applications, in general – with a particular interest in archaeological textiles. We overview existing computational methods, their applications and limitations. Afterward, based on the current knowledge gaps, we discuss where the field should go next.

Gloss perception is a complex psychovisual phenomenon, whose mechanisms are not yet fully explained. Instrumentally measured surface reflectance is usually poor predictor of human perception of gloss. The state-of-the-art studies demonstrate that, in addition to surface reflectance, object's shape and illumination geometry also affect the magnitude of gloss perceived by the human visual system (HVS). Recent studies attribute this to image cues â the specific regularities in image statistics that are generated by a combination of these physical properties, and that, in their part, are proposedly used by the HVS for assessing gloss. Another study has recently demonstrated that subsurface scattering of light is an additional factor that can play the role in perceived gloss, but the study provides limited explanation of this phenomenon. In this work, we aimed to shed more light to this observation and explain why translucency impacts perceived gloss, and why this impact varies among shapes. We conducted four psychophysical experiments in order to explore whether image cues typical for opaque objects also explain the variation of perceived gloss in translucent objects and to quantify how these cues are modulated by the subsurface scattering properties. We found that perceived contrast, coverage area, and sharpness of the highlights can be combined to reliably predict perceived gloss. While sharpness is the most significant cue for assessing glossiness of spherical objects, coverage is more important for a complex Lucy shape. Both of these observations propose an explanation why subsurface scattering albedo impacts perceived gloss.

We introduce an innovative multi-resolution framework for encoding and interactively visualizing large relightable images using a neural reflectance model derived from a state-of-the-art technique. The framework is seamlessly integrated into a scalable multi-platform framework that supports adaptive streaming and exploration of multi-layered relightable models in web settings. To enhance efficiency, we optimized the neural model, simplified decoding, and implemented a custom WebGL shader specific to the task, eliminating the need for deep-learning library integration in the code. Additionally, we introduce an efficient level-of-detail management system supporting fine-grained adaptive rendering through on-the-fly resampling in latent feature space. The resulting viewer facilitates interactive neural relighting of large images. Its modular design allows the incorporation of functionalities for cultural heritage analysis, such as loading and simultaneous visualization of multiple relightable layers with arbitrary rotations.

Archeological textiles can provide invaluable insight into the past. However, they are often highly fragmented, and a puzzle has to be solved to re-assemble the object and recover the original motifs. Unlike common jigsaw puzzles, archeological fragments are highly damaged, and no correct solution to the puzzle is known. Although automatic puzzle solving has fascinated computer scientists for a long time, this work is one of the first attempts to apply modern machine learning solutions to archeological textile re-assembly. First and foremost, it is important to know which fragments belong to the same object. Therefore, features are extracted from digital images of textile fragments using color statistics, classical texture descriptors, and deep learning methods. These features are used to conduct clustering and identify similar fragments. Four different case studies with increasing complexity are discussed in this article: from well-preserved textiles with available ground truth to an actual open problem of Oseberg archeological tapestry with unknown solution. This work reveals significant knowledge gaps in current machine learning, which helps us to outline a future avenue toward more specialized application-specific models.