Adaptive Sampling for Monte-Carlo Event Imagery Rendering

This paper presents a novel event-based camera simulation system based on physically accurate Monte Carlo path tracing with adaptive path sampling. The adaptive sampling performed in the proposed method is based on the probability of event occurrence. First, our rendering system collects logarithmic luminances rather than raw luminance based on the circuit characteristics of event-based cameras. We calculate the probability of how much different the logarithmic luminance gap is from the preset event threshold. This means how likely an event will occur at the pixel. Then, we sample paths adaptively based on the sample rate combined with a previous adaptive sampling method. We demonstrate that our method achieves higher rendering quality than the baseline approach which allocates path samples uniformly for each pixel.