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Head-mounted displays (HMDs) are becoming more and more popular as a device for displaying a virtual reality space, but how real are they? The present study attempted to quantitatively evaluate the degree of reality achieved with HMDs by using a perceptual phenomenon as a measure. Lightness constancy is an ability that is present in human visual perception, in which the perceived reflectance (i.e., the lightness) of objects appears to stay constant across illuminant changes. Studies on color/lightness constancy in humans have shown that the degree of constancy is high, in general, when real objects are used as stimuli. We asked participants to make lightness matches between two virtual environments with different illuminant intensities, as presented in an HMD. The participants' matches showed a high degree of lightness constancy in the HMD; our results marked no less than 74.2% (84.8% at the maximum) in terms of the constancy index, whereas the average score on the computer screen was around 65%. The effect of head-tracking ability was confirmed by disabling that function, and the result showed a significant drop in the constancy index but that it was equally effective when the virtual environment was generated by replay motions. HMDs yield a realistic environment, with the extension of the visual scene being accompanied by head motions.
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http://dx.doi.org/10.3390/jimaging10020036 | DOI Listing |
Annu Rev Vis Sci
August 2025
Centre for Transformative Neuroscience, Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom;
The contributions of surface reflectance and incident illumination are entangled in the light reflected to the eye. Historically, the extent to which the perception of one determines the other has long been debated, particularly in empirical studies of surface lightness and color constancy. Despite enormous progress in physical measurements of the spatial, spectral, and temporal properties of natural illumination, and in the ability to generate and control in real time artificial light of an almost infinite variety of spectra, the questions of whether and how people perceive the illumination as a distinct entity with its own color, and the interdependence of perceived surface color on perceived illumination, remain open.
View Article and Find Full Text PDFIperception
July 2025
Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France.
The image intensity depends on the illumination, the reflectance properties of objects but also on the reflectance and absorption properties of any intervening media. In this study we present observers with glossy objects behind partially transmissive materials. The transparent layer causes an achromatic color shift and compression in luminance contrast, which can affect the perception of the specular reflections of the object behind the layer.
View Article and Find Full Text PDFSci Rep
July 2025
Department of Orthodontics, Saint Louis University, St. Louis, USA.
This study investigates the mechanical and viscoelastic properties of TC-85, a biocompatible material specifically designed for orthodontic applications, with a focus on how temperature variations influence its mechanical and viscoelastic properties and their relevance to clinical outcomes. Using a Digital Light Processing (DLP) 3D printer, the photosensitive resin TC-85 is printed, and extensive thermo-mechanical testing is conducted, which includes evaluations of tensile modulus, stress relaxation, and creep behavior. Dynamic Mechanical Analysis (DMA) is conducted at temperatures varying from 30 to 45 °C to assess the material's adaptive response to thermal fluctuations.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
June 2025
Color constancy, the human visual system's ability to perceive consistent colors under varying illumination conditions, is crucial for accurate color perception. Recently, deep learning algorithms have been introduced into this task and have achieved remarkable achievements. However, existing methods are limited by the scale of current multi-illumination datasets and model size, hindering their ability to learn discriminative features effectively and their practical value for deployment in cameras.
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