{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/itof-flow-based-high-frame-rate-depth-imaging","title":"iToF-flow-based High Frame Rate Depth Imaging","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Yu Meng","Zhou Xue","Xu Chang","Xuemei Hu","Tao Yue"],"abstract":"    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.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Meng_iToF-flow-based_High_Frame_Rate_Depth_Imaging_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Meng_iToF-flow-based_High_Frame_Rate_Depth_Imaging_CVPR_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"itof-flow-based-high-frame-rate-depth-imaging","repo_url":"https://github.com/computationalperceptionlab/itof_flow","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}