Compression and denoising of time-resolved light transport

Exploiting temporal information of light propagation captured at ultra-fast frame rates has enabled applications such as reconstruction of complex hidden geometry and vision through scattering media. However, these applications require high-dimensional and high-resolution transport data, which introduces significant performance and storage constraints. Additionally, due to different sources of noise in both captured and synthesized data, the signal becomes significantly degraded over time, compromising the quality of the results. In this work, we tackle these issues by proposing a method that extracts meaningful sets of features to accurately represent time-resolved light transport data. Our method reduces the size of time-resolved transport data up to a factor of 32, while significantly mitigating variance in both temporal and spatial dimensions.