Investigating lighting calibration opportunities in virtual reality for real-world illumination fidelity: an empirical study of variable lighting arrangements

Virtual reality (VR) offers transformative potential for human behavioral studies by enabling controlled simulations of complex environments. Accurate replication of real-world conditions, however, remains critical, particularly for lighting studies that require high precision and reliable data extraction. This study investigates discrepancies in light distribution across typical lighting configurations and develops predictive models to mitigate these variations while identifying practical constraints. Illumination differences between real-world measurements, DIALux evo simulations, and VR environments were examined empirically. Multiple linear regression with interaction terms was then used to develop predictive models for one-, two-, and four-luminaire configurations on horizontal and vertical planes, based on 100 test points per plane in a controlled room. Model validation included cross-space application and residual analysis using an additional dataset of 60 test points per plane collected in a separate room with a similar lighting setup. Statistical analysis revealed 53%–88% differences in illumination intensity across configurations. The predictive models effectively reduced discrepancies on the horizontal plane and were optimized for linear, low-intensity lighting, while highlighting the need for further investigation into vertical illumination and complex luminaire arrangements. Future work involving non-linear or advanced computational approaches could enhance model accuracy for irregular lighting distributions in VR. These findings have practical implications for VR-based lighting studies, building performance simulations, and virtual illumination design.