iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging

TL;DR

This paper introduces iToF2dToF, a novel data-driven approach that enhances indirect ToF depth sensing by leveraging transient image representations, improving robustness against noise and multi-path interference in real-world scenarios.

Contribution

The paper revisits the transient representation in iToF imaging, proposing a flexible, data-driven method to interpolate frequencies and estimate transient images, compatible with various depth algorithms.

Findings

Outperforms previous methods in real depth sensing scenarios.

Effectively mitigates multi-path interference and low SNR issues.

Flexible integration with different depth sensing algorithms.

Abstract

Indirect Time-of-Flight (iToF) cameras are a promising depth sensing technology. However, they are prone to errors caused by multi-path interference (MPI) and low signal-to-noise ratio (SNR). Traditional methods, after denoising, mitigate MPI by estimating a transient image that encodes depths. Recently, data-driven methods that jointly denoise and mitigate MPI have become state-of-the-art without using the intermediate transient representation. In this paper, we propose to revisit the transient representation. Using data-driven priors, we interpolate/extrapolate iToF frequencies and use them to estimate the transient image. Given direct ToF (dToF) sensors capture transient images, we name our method iToF2dToF. The transient representation is flexible. It can be integrated with different rule-based depth sensing algorithms that are robust to low SNR and can deal with ambiguous scenarios that arise in practice (e.g., specular MPI, optical cross-talk). We demonstrate the benefits of iToF2dToF over previous methods in real depth sensing scenarios.