Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments

TL;DR

This paper introduces a learning-based method that fuses noisy indirect Time-of-Flight signals with RGB images to significantly improve long-range depth estimation in challenging real-world environments.

Contribution

It presents a novel end-to-end depth prediction network that effectively combines I-ToF data and RGB images, addressing noise and signal sparsity in difficult conditions.

Findings

Achieves over 40% RMSE improvement in depth accuracy

Effective fusion of I-ToF and RGB data in challenging scenarios

Demonstrates robustness to ambient light and long-range conditions

Abstract

Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price. Previous works have mainly focused on quality improvement for I-ToF imaging especially curing the effect of Multi Path Interference (MPI). These investigations are typically done in specifically constrained scenarios at close distance, indoors and under little ambient light. Surprisingly little work has investigated I-ToF quality improvement in real-life scenarios where strong ambient light and far distances pose difficulties due to an extreme amount of induced shot noise and signal sparsity, caused by the attenuation with limited sensor power and light scattering. In this work, we propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image and fuses their latent representation based on a multi step approach involving both implicit and explicit alignment to predict a high quality long range depth map aligned to the RGB viewpoint. We test our approach on challenging real-world scenes and show more than 40% RMSE improvement on the final depth map compared to the baseline approach.