Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement

Single-shot high-speed 3D imaging is important for reconstructions of dynamic objects.For fringe projection profilometry(FPP),however,it is still challenging to recover accurate 3D shapes of isolated objects by a single fringe image.In this paper,we demonstrate that the deep neural networks can be trained to directly recover the absolute phase from a unique fringe image that involves spatially multiplexed fringe patterns of different frequencies.The extracted phase is free from spectrum-aliasing problem which is hard to avoid for traditional spatial-multiplexing methods.Experiments on both static and dynamic scenes show that the proposed approach is robust to object motion and can obtain high-quality 3D reconstructions of isolated objects within a single fringe image.

Systematic Radio Telescope Alignment Using Portable Fringe Projection Profilometry

In 2019,the Event Horizon Telescope(EHT)released the first-ever image of a black hole event horizon.Astronomers are now aiming for higher angular resolutions of distant targets,like black holes,to understand more about the fundamental laws of gravity that govern our universe.To achieve this higher resolution and increased sensitivity,larger radio telescopes are needed to operate at higher frequencies and in larger quantities.Projects like the next-generation Very Large Array(ngVLA)and the Square-Kilometer Array(SKA)require building hundreds of telescopes with diameters greater than 10 ms over the next decade.This has a twofold effect.Radio telescope surfaces need to be more accurate to operate at higher frequencies,and the logistics involved in maintaining a radio telescope need to be simplified to support them properly in large quantities.Both of these problems can be solved with improved methods for surface metrology that are faster and more accurate with a higher resolution.This leads to faster and more accurate panel alignment and,therefore,a more productive observatory.In this paper,we present the use of binocular fringe projection profilometry as a solution to this problem and demonstrate it by aligning two panels on a 3-m radio telescope dish.The measurement takes only 10 min and directly delivers feedback on the tip,tilt,and piston of each panel to create the ideal reflector shape.

High-speed three-dimensional shape measurement with inner shifting-phase fringe projection profilometry

Fringe projection profilometry(FPP)has been extensively studied in the field of three-dimensional(3D)measurement.Although FPP always uses high-frequency fringes to ensure high measurement accuracy,too many patterns are projected to unwrap the phase,which affects the speed of 3D reconstruction.We propose a high-speed 3D shape measurement method using only three high-frequency inner shifting-phase patterns(70 periods),which satisfies both high precision and high measuring speed requirements.Besides,our proposed method obtains the wrapped phase and the fringe order simultaneously without any other information and constraints.The proposed method has successfully reconstructed moving objects with high speed at the camera's full frame rate(1700 frames per second).

Deep-learning-enabled temporally super-resolved multiplexed fringe projection profilometry:high-speed kHz 3D imaging with low-speed camera

Recent advances in imaging sensors and digital light projection technology have facilitated rapid progress in 3D optical sensing,enabling 3D surfaces of complexshaped objects to be captured with high resolution and accuracy.Nevertheless,due to the inherent synchronous pattern projection and image acquisition mechanism,the temporal resolution of conventional structured light or fringe projection profilometry(FPP)based 3D imaging methods is still limited to the native detector frame rates.In this work,we demonstrate a new 3D imaging method,termed deep-learning-enabled multiplexed FPP(DLMFPP),that allows to achieve high-resolution and high-speed 3D imaging at near-one-order of magnitude-higher 3D frame rate with conventional low-speed cameras.By encoding temporal information in one multiplexed fringe pattern,DLMFPP harnesses deep neural networks embedded with Fourier transform,phase-shifting and ensemble learning to decompose the pattern and analyze separate fringes,furnishing a high signal-to-noise ratio and a ready-to-implement solution over conventional computational imaging techniques.We demonstrate this method by measuring different types of transient scenes,including rotating fan blades and bullet fired from a toy gun,at kHz using cameras of around 100 Hz.Experiential results establish that DLMFPP allows slow-scan cameras with their known advantages in terms of cost and spatial resolution to be used for high-speed 3D imaging tasks.

Deep Learning-Based Invalid Point Removal Method for Fringe Projection Profilomet y

Fringe projection profilometry(FPP)has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed.The point cloud,which is a measurement result of the FPP system,typically contains a large number of invalid points caused by the background,ambient light,shadows,and object edge regions.Research on noisy point detection and elimination has been conducted over the past two decades.However,existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark.In this paper,we propose a novel invalid point removal framework that consists of two aspects:(1)A convolutional neural network(CNN)is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions.(2)A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects.Experimental results verify that the proposed framework(1)can remove background points intelligently and accurately in different types of complex circumstances,and(2)performs excellently in discrete point detection from object regions.