Neural relighting methods for mixed reality flight simulators
Objective The application of mixed reality(MR)in training environments,particularly in the field of aviation,marks a remarkable leap from traditional simulation models.This innovative technology overlays virtual elements onto the real world,creating a seamless interactive experience that is critical in simulating high-risk scenarios for pilots.Despite its advances,the integration of real and virtual elements often suffers from inconsistencies in lighting,which can disrupt the user s sense of presence and diminish the effectiveness of training sessions.Prior attempts to reconcile these differences have involved static solutions that lack adaptability to the dynamic range of real-world lighting conditions encountered dur-ing flight.This study is informed by a comprehensive review of current methodologies,including photometric alignment techniques and the adaptation of CGI(computer-generated imagery)elements using standard graphics pipelines.Our analysis identified a gap in real-time dynamic relighting capabilities,which we address through a novel neural network-based approach.Method The methodological core of this research is the development of an advanced neural network archi-tecture designed for the sophisticated task of image relighting.The neural network architecture proposed in this research is a convolutional neural network variant,specifically tailored to process high-fidelity images in a manner that retains critical details while adjusting to new lighting conditions.Meanwhile,an integral component of our methodology was the generation of a comprehensive dataset specifically tailored for the relighting of fighter jet cockpit environments.To ensure a high degree of realism,we synthesized photorealistic renderings of the cockpit interior under a wide array of atmospheric condi-tions,times of day,and geolocations across different latitudes and longitudes.This synthetic dataset was achieved by inte-grating our image capture process with an advanced weather simulation system,which allowed us to replicate the intricate effects of natural and artificial lighting as experienced within the cockpit.The resultant dataset presents a rich variety of lighting scenarios,ranging from the low-angle illumination of a sunrise to the diffused lighting of an overcast sky,providing our neural network with the nuanced training required to emulate real-world lighting dynamics accurately.The neural net-work is trained with this dataset to understand and dissect the complex interplay of lighting and material properties within a scene.The first step of the network involves a detailed decomposition of input images to separate and analyze the compo-nents affected by lighting,such as shadows,highlights,and color temperature.The geometry of the scene,the textures,and how objects occlude or reflect light must be deduced,extracting these elements into a format that can be manipulated independently of the original lighting conditions.To actualize the target lighting effect,the study leverages a concept adapted from the domain of precomputed radiance transfer——a technique traditionally used for rendering scenes with com-plex light interactions.By estimating radiance transfer functions at each pixel and representing these as coefficients over a series of spherical harmonic basis functions,the method facilitates a rapid and accurate recalculation of lighting across the scene.The environmental lighting conditions,captured through high dynamic range imaging techniques,are also projected onto these spherical harmonic functions.This approach allows for the real-time adjustment of lighting by simply recalculat-ing the dot product of these coefficients,corresponding to the new lighting environment.This step is a computational break-through because it circumvents the need for extensive ray tracing or radiosity calculations,which are computationally expen-sive and often impractical for real-time applications.This method stands out for its low computational overhead,enabling near real-time relighting that can adjust dynamically as the simulated conditions change.Result The empirical results achieved through this method are substantiated through a series of rigorous tests and comparative analyses.The neural net-work's performance was benchmarked against traditional and contemporary relighting methods across several scenarios reflecting diverse lighting conditions and complexities.The model consistently demonstrated superior performance,not only in the accuracy of light replication but also in maintaining the fidelity of the original textures and material properties.The visual quality of the relighting was assessed through objective performance metrics,including comparison of luminance distribution,color fidelity,and texture preservation against ground truth datasets.These metrics consistently indicated a remarkable improvement in visual coherence and a reduction in artifacts,ensuring a more immersive experience without the reliance on subjective user studies.Conclusion The implemented method effectively resolves the challenge of inconsistent lighting conditions in MR flight simulators.It contributes to the field by enabling dynamic adaptation of real-world images to the lighting conditions of virtual environments.This research not only provides a valuable tool for enhancing the realism and immersion of flight simulators but also offers insights that could benefit future theoretical and practical advancements in MR technology.The study utilized spherical harmonic coefficients of environmental light maps to convey lighting condition information and pioneered the extraction of scene radiance lighting functions'spherical harmonic coefficients from real image data.This validated the feasibility of predicting scene radiance transfer functions from real images using neural net-works.The limitations and potential improvements of the current method are discussed,outlining directions for future research.For example,considering the temporal continuity present in the relighted images,future efforts could exploit this characteristic to optimize the neural network architecture,integrating modules that enhance the stability of the prediction results.
relightingneural rendering methodsradiance transfer functionsmixed reality(MR)flight simulator
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