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Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depthestimation for mobile devices due to its small size and affordable price.Previous works have mainly focused on quality improvement for I-ToF imagingespecially curing the effect of Multi Path Interference (MPI). Theseinvestigations are typically done in specifically constrained scenarios atclose distance, indoors and under little ambient light. Surprisingly littlework has investigated I-ToF quality improvement in real-life scenarios wherestrong ambient light and far distances pose difficulties due to an extremeamount of induced shot noise and signal sparsity, caused by the attenuationwith limited sensor power and light scattering. In this work, we propose a newlearning based end-to-end depth prediction network which takes noisy raw I-ToFsignals as well as an RGB image and fuses their latent representation based ona multi step approach involving both implicit and explicit alignment to predicta high quality long range depth map aligned to the RGB viewpoint. We test ourapproach on challenging real-world scenes and show more than 40% RMSEimprovement on the final depth map compared to the baseline approach.

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