Hyperspectral rendering is the only way to simulate optics and sensors accurately. Faithfully simulating the sensor is a great way to reduce the domain gap in your synthetic datasets, generating images closer to what the real cameras would take. Training with such images gives you a better chance that your perception deep learning model will generalize to real-world images, improving your perception system’s performance cost-effectively.
Images generated from hyperspectral data are richer and more accurate since the information used to calculate the color of every pixel includes all wavelengths sampled by the render, unlike other non-hyperspectral ray tracing render engines that can generate nice images with less accuracy.