Structure-Aware Parametric Representations for Time-Resolved Light Transport

TL;DR

This paper introduces a noise-robust, compact representation of time-resolved illumination using mixtures of exponentially-modified Gaussians, enabling improved hidden scene reconstruction and depth estimation.

Contribution

The proposed method offers a novel, compact, and noise-robust representation of time-resolved light transport data, significantly reducing data size while preserving structural information.

Findings

Representation is two orders of magnitude smaller than discretized data.

Achieves consistent results in hidden scene reconstruction.

Provides quantitative improvements over previous methods.

Abstract

Time-resolved illumination provides rich spatio-temporal information for applications such as accurate depth sensing or hidden geometry reconstruction, becoming a useful asset for prototyping and as input for data-driven approaches. However, time-resolved illumination measurements are high-dimensional and have a low signal-to-noise ratio, hampering their applicability in real scenarios. We propose a novel method to compactly represent time-resolved illumination using mixtures of exponentially-modified Gaussians that are robust to noise and preserve structural information. Our method yields representations two orders of magnitude smaller than discretized data, providing consistent results in applications such as hidden scene reconstruction and depth estimation, and quantitative improvements over previous approaches.