Lightness constancy in reality, in virtual reality, and on flat-panel displays.

Virtual reality (VR) displays are being used in an increasingly wide range of applications. However, previous work shows that viewers often perceive scene properties very differently in real and virtual environments and so realistic perception of virtual stimuli should always be a carefully tested conclusion, not an assumption. One important property for realistic scene perception is surface color. To evaluate how well virtual platforms support realistic perception of achromatic surface color, we assessed lightness constancy in a physical apparatus with real lights and surfaces, in a commercial VR headset, and on a traditional flat-panel display. We found that lightness constancy was good in all three environments, though significantly better in the real environment than on the flat-panel display. We also found that variability across observers was significantly greater in VR and on the flat-panel display than in the physical environment. We conclude that these discrepancies should be taken into account in applications where realistic perception is critical but also that in many cases VR can be used as a flexible alternative to flat-panel displays and a reasonable proxy for real environments.

Luminance calibration of virtual reality displays in Unity.

Virtual reality (VR) displays are an increasingly popular medium for experiments on visual perception. This presents the challenge of showing precisely controlled stimuli on devices that were not primarily designed for research. Here we describe methods for controlling stimulus luminance in VR experiments created in Unity using the Built-in Render Pipeline. We discuss the Gamma/Linear setting, measuring luminance in a VR headset, and using color grading in Unity's Post-Processing Stack to make stimulus luminance proportional to achromatic RGB value. We provide MATLAB code that uses luminance measurements from a VR headset to generate the lookup table that Unity requires for linearizing luminance. We emphasize that when creating experiments in this complex environment, it is important to experiment with the rendering process to confirm that stimuli are displayed as expected. We show results of several such tests and provide code as a starting point for readers who wish to run further tests related to their own research.

Lightness matching and perceptual similarity.

Lightness constancy is the ability to perceive surface reflectance correctly despite substantial changes in lighting intensity. A classic view is that lightness constancy is the result of a "discounting" of lighting intensity, and this continues to be a prominent view today. Logvinenko and Maloney (2006) have proposed an alternative approach to understanding lightness constancy, in which observers do not make explicit estimates of reflectance, and lightness constancy is instead based on a perceptual similarity metric that depends on both the reflectance and the illuminance of surfaces viewed under different lighting conditions. Here we compare these two views using a novel, free-adjustment reflectance-matching task. We test whether observers can match reflectance in a task where they are free to adjust both the illuminance and the reflectance of the match stimulus over a wide range. We find that observers can match reflectance under these conditions, which supports the view that observers make explicit estimates of reflectance. We also compare performance in this free adjustment task using physical objects and computer-rendered images as stimuli. We find that lightness constancy is good in both cases, but with some evidence of a glow-related artifact with computer-rendered stimuli.