Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising
This work addresses efficient simulation of event-based cameras for computer vision and graphics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of generating event-based video from noisy Monte Carlo path-traced frames by extending a weighted local regression denoising method to detect brightness changes, reducing computational cost while maintaining or improving performance compared to exhaustive denoising.
This paper presents an algorithm to obtain an event-based video from noisy frames given by physics-based Monte Carlo path tracing over a synthetic 3D scene. Given the nature of dynamic vision sensor (DVS), rendering event-based video can be viewed as a process of detecting the changes from noisy brightness values. We extend a denoising method based on a weighted local regression (WLR) to detect the brightness changes rather than applying denoising to every pixel. Specifically, we derive a threshold to determine the likelihood of event occurrence and reduce the number of times to perform the regression. Our method is robust to noisy video frames obtained from a few path-traced samples. Despite its efficiency, our method performs comparably to or even better than an approach that exhaustively denoises every frame.