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Hyperspectral imaging empowers computer vision systems with the distinctcapability of identifying materials through recording their spectralsignatures. Recent efforts in data-driven spectral reconstruction aim atextracting spectral information from RGB images captured by cost-effective RGBcameras, instead of dedicated hardware.  In this paper we systematically analyze the performance of such methods,evaluating both the practical limitations with respect to current datasets andoverfitting, as well as fundamental limits with respect to the nature of theinformation encoded in the RGB images, and the dependency of this informationon the optical system of the camera.  We find that the current models are not robust under slight variations, e.g.,in noise level or compression of the RGB file. Both the methods and thedatasets are also limited in their ability to cope with metameric colors. Thisissue can in part be overcome with metameric data augmentation. Moreover,optical lens aberrations can help to improve the encoding of the metamericinformation into the RGB image, which paves the road towards higher performingspectral imaging and reconstruction approaches.

Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysis