High-fidelity color characterization in virtual reality across head mounted displays, game engines, and materials.

We present a comprehensive colorimetric analysis of three head mounted displays (HMDs) - HTC Vive Pro Eye, Pimax 8K X DMAS, and Varjo Aero - focusing on their color calibration and uniformity across different game engines (Unity and Unreal) and for different materials/shaders. We developed a robust methodology combining hardware and software tools, including spectroradiometry and imaging colorimetry, to characterize and calibrate these HMDs for accurate color reproduction. The study showcases substantial advancements in colorimetric accuracy, with a reduction in the average deltaE00 of 90 or more across all tested HMDs and conditions.

Color constancy mechanisms in virtual reality environments.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time.

Colour Calibration of a Head Mounted Display for Colour Vision Research Using Virtual Reality.

Virtual reality (VR) technology offers vision researchers the opportunity to conduct immersive studies in simulated real-world scenes. However, an accurate colour calibration of the VR head mounted display (HMD), both in terms of luminance and chromaticity, is required to precisely control the presented stimuli. Such a calibration presents significant new challenges, for example, due to the large field of view of the HMD, or the software implementation used for scene rendering, which might alter the colour appearance of objects.

Deciphering image contrast in object classification deep networks.

The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal activity. A comparable level of complexity also emerges in deep neural networks (DNNs) while exhibiting human-level performance in demanding visual tasks. Unlike in biological systems, all parameters and operations of DNNs are accessible.

Color matching images with unknown non-linear encodings.

We present a color matching method that deals with different non-linear encodings. In particular, given two different views of the same scene taken by two cameras with unknown settings and internal parameters, and encoded with unknown non-linear curves, our method is able to correct the colors of one of the images making it look as if it was captured under the other camera's settings. Our method is based on treating the in-camera color processing pipeline as a concatenation of a matrix multiplication on the linear image followed by a non-linearity.