Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-persecond 3D imaging with deep learning  

作　　者：Bowen Wang Wenwu Chen Jiaming Qian Shijie Feng Qian Chen Chao Zuo 

机构地区：[1]Smart Computational Imaging Laboratory(SCILab),Nanjing University of Science and Technology,Nanjing,Jiangsu Province,China [2]Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense,Nanjing University of Science and Technology,Nanjing,Jiangsu Province,China

出　　处：《Light(Science & Applications)》2025年第2期551-563,共13页光(科学与应用)(英文版)

基　　金：supported by National Key Research and Development Program of China(2022YFB2804603,2022YFA1205002,2024YFE0101300);National Natural Science Foundation of China(U21B2033,62075096,62105151,62175109,62227818,62361136588);Leading Technology of Jiangsu Basic Research Plan(BK20192003);"333 Engineering"Research Project of Jiangsu Province(BRA2016407);Jiangsu Provincial"One belt and one road"innovation cooperation project(BZ2020007);Fundamental Research Funds for the Central Universities(30921011208,30919011222,30920032101);Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(USGP202105,JSGP202201).

摘　　要：To reveal the fundamental aspects hidden behind a variety of transient events in mechanics,physics,and biology,the highly desired ability to acquire three-dimensional(3D)images with ultrafast temporal resolution has been long sought.As one of the most commonly employed 3D sensing techniques,fringe projection profilometry(FPP)reconstructs the depth of a scene from stereo images taken with sequentially structured illuminations.However,the imaging speed of current FPP methods is generally capped at several kHz,which is limited by the projector-camera hardware and the number of fringe patterns required for phase retrieval and unwrapping.Here we report a novel learning-based ultrafast 3D imaging technique,termed single-shot super-resolved FPP(SSSR-FPP),which enables ultrafast 3D imaging at 100,000 Hz.SSSR-FPP uses only one pair of low signal-to-noise ratio(SNR),low-resolution,and pixelated fringe patterns as input,while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network.Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras,while"regenerating"the lost spatial resolution through deep learning.To demonstrate the high spatio-temporal resolution of SSSR-FPP,we present 3D videography of several transient scenes,including rotating turbofan blades,exploding building blocks,and the reciprocating motion of a steam engine,etc.,which were previously challenging or even impossible to capture with conventional methods.Experimental results establish SSSR-FPP as a significant step forward in the field of 3D optical sensing,offering new insights into a broad spectrum of dynamic processes across various scientific disciplines.

关 键 词：PHYSICS reveal fundamental aspects deep learning transient events ultrafast D imaging stereo images 

分 类 号：O439[机械工程—光学工程]