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Lu et al. (2025) proved that HDRI camera sensors from different viewpoints can capture consistent and transferable luminance patterns in daylit spaces through Conditional Generative Adversarial Networks (CGANs). Building on that, this paper validates that non-intrusive luminance monitoring can be used to evaluate daylighting preferences, using collected experimental datasets with human subjects at different seating locations in a real open-plan office. To apply paired comparisons for effective learning, subjects compared successive pairs of different visual conditions and indicated their visual preferences through online surveys. Meanwhile, ten small, low-cost, and calibrated cameras captured luminance maps from both the field of view (FOV) of each occupant and non-intrusive viewpoints (on computer monitors, luminaire/ceiling and desk) under various sky conditions and interior luminance distributions. Convolutional Neural Network (CNN) models were developed and trained on luminance similarity index maps (generated from pixel-wise comparisons between successive luminance maps captured from FOV and non-intrusive cameras separately), to classify each subject’s daylight visual preferences. The results showed that the models trained on luminance distributions measured by monitor-mounted and ceiling-mounted cameras produced preference predictions consistent with those derived from FOV cameras, and can reliably learn visual preferences (83-94% accuracy) in all cases except for locations furthest from the windows. Overall, this study is the first to demonstrate that daylight preferences can be learned non-invasively by employing the full potential of HDRI and deep learning techniques, marking a significant milestone toward practical, AI-assisted, human-centered daylighting operation. 

Luminance monitoring within the field of view (FOV) is required for assessing visual comfort and overall visual preferences, but it is practically challenging and intrusive. As a result, real-time, human-centered daylighting operation remains a challenge. This paper presents a novel deep-learning based framework method to demonstrate that meaningful features in the occupant’s visual field can be extracted without invasive measurements. It is the first proof of concept to show that it is feasible to monitor luminance distributions as perceived by people, using a non-intrusive camera integrated with deep learning neural networks. A Conditional Generative Adversarial Network (CGAN), pix2pix is used to transfer information from non-intrusive images to FOV images. Two datasets were collected in an open-plan office with compact, low-cost High Dynamic Range Image (HDRI) cameras installed at two alternate locations (a wall or a monitor), to separately train two pix2pix models with the same target FOV images. The results show that the generated FOV images closely resemble the measured FOV images in terms of pixelwise luminance errors, mean luminance, and structural similarity. The main errors are due to bright scenes, visible through windows, confined to a very limited number of pixels. Overall, this work establishes a basis for future studies to assess the effect of visual environment on human perception using non-intrusive measurements. It also provides the theoretical foundation for a connected paper (Lu et al., 2025), which demonstrates that non-intrusive measurements and deep learning techniques can be used to discover daylight preferences and enable AI-assisted daylighting operation.