iToF-flow-based High Frame Rate Depth Imaging

iToF is a prevalent, cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process, we propose the iToF flow, composed of crossmode transformation and uni-mode photometric correction, to model the variation of measurements caused by different measurement modes and 3D motion, respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow, and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments, including both simulation and real-world experiments, are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method, our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://github.com/ComputationalPerceptionLab/iToF_flow.