Continuous Levels of Detail for Light Field Networks
David Li Brandon Y. Feng Amitabh Varshney
Recently, several approaches have emerged for generating neural
representations with multiple levels of detail (LODs). LODs can improve the
rendering by using lower resolutions and smaller model sizes when appropriate.
However, existing methods generally focus on a few discrete LODs which suffer
from aliasing and flicker artifacts as details are changed and limit their
granularity for adapting to resource limitations. In this paper, we propose a
method to encode light field networks with continuous LODs, allowing for finely
tuned adaptations to rendering conditions. Our training procedure uses
summed-area table filtering allowing efficient and continuous filtering at
various LODs. Furthermore, we use saliency-based importance sampling which
enables our light field networks to distribute their capacity, particularly
limited at lower LODs, towards representing the details viewers are most likely
to focus on. Incorporating continuous LODs into neural representations enables
progressive streaming of neural representations, decreasing the latency and
resource utilization for rendering.