Realism in Daylight Modelling: Nature and Buildings

This event comprises two presentations describing ultra-realism in the modelling of daylight for very different contexts:

1. The representation of 3D geometrical and material descriptions for accurate CBDM of buildings, by Nima Forouzandeh, Delft University of Technology, NL
2. The accurate reproduction of luminous light patterns, such as sunlight filtering through leaves, for perception-based studies, by Sietse de Vries, Eindhoven University of Technology, NL.

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Realism in Daylight Modelling: Buildings

Speaker: Nima Forouzandeh

Climate-based Daylight Models (CBDM) in existing indoor spaces pose several practical challenges despite the maturity of simulation engines. In practice, many of such bottlenecks pertain acquisition and formalised representation of geometrical information and material optical properties. In his talk, Nima will present the methods and findings of his recently concluded doctorate in four key parts that address such bottlenecks. The first is about introducing a data-driven definition of geometrical levels of detail along with a sensitivity analysis of daylight metrics to different levels of geometrical detail. The second focuses on quantifying how uncertainty in material optical properties propagates into annual daylight results. The third will be about a semi-automatic pipeline and software he developed for reconstructing simulation-ready geometry from point clouds. The fourth evaluates light weight image-based techniques for estimating material reflectance and their influence on daylight simulation, including spectral uplifting from RGB inputs to support non-visual daylight simulations and analyses. These studies develop and assess semi-automatic workflows for the creation, characterisation, and calibration of indoor daylight models, with the overarching aim of reducing error, effort, and time in daylight modelling practice.

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Realism in Daylight Modelling: Nature

Speaker: Sietse de Vries

Dynamic luminous patterns produced by natural lighting, such as shadows cast by sunlight filtering through leaves swaying in the wind, have been linked to positive perceptual responses including visual interest, improved mood, and stress restoration. This has motivated growing interest in recreating such patterns indoors through dynamic lighting design. Doing so well requires understanding which spatial and temporal properties of these patterns drive specific perceptual responses, which in turn requires controlled experiments with consistent conditions across many participants. Real-world natural lighting cannot provide this control, but immersive virtual reality (VR) can — provided the simulated lighting is physically accurate. Radiance, the standard physically based lighting simulation tool in the scientific community, is a natural candidate. But generating dynamic luminous patterns with it is laborious, and its accuracy for this novel use case has not been established. We present RADYNVR (Radiance Dynamic Virtual Reality), a Radiance-based tool that streamlines the creation of VR environments with accurate dynamic natural lighting, enabling systematic perceptual research on these patterns.

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