Reliability-guided phase-unwrapping algorithm for the measurement of discontinuous three-dimensional objects

Cui, Haihua; Liao, Wenhe; Dai, Ning; Cheng, Xiaosheng

doi:10.1117/1.3591950

Cited by 29 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this idea, phase unwrapping can be carried out from one pixel to another following a certain path on ( , ) xy and ( , ) xy are two adjacent pixels on the path, the following provides a straight-forward method to unwrap pixel ( , ) xy from ( ', ') xy [14]: xy can be obtained.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…Existing phase unwrapping algorithms can be classified into two categories: the temporal algorithms (such as gray-code method [2] and multi-frequency method [3]) and spatial algorithms including simple path-following method [6], branch-cut method [7], and quality-guided flood-fill (QGFF) method [14]. The temporal algorithms are able to accurately recover the true phases from complex wrapped phase maps.…”

Section: Introductionmentioning

confidence: 99%

“…However, such a method requires that phase value varies smoothly on the wrapped phase map. In the cases of complex phase maps, the QGFF method [5,10,12,14] can be employed, which has attracted intensive studies. With QGFF method, every pixel on the phase maps is associated with a quality parameter measuring the smoothness of the pixel compared with its surrounding ones.…”

mentioning

confidence: 99%

See 2 more Smart Citations

An object image edge detection based quality-guided phase unwrapping approach for fast three-dimensional measurement

Chen

Song

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

View full text Add to dashboard Cite

Abstract²A new approach for achieving fast and reliable unwrapping of complex phase maps is presented for phase shifted based three-dimensional (3D) Fringe pattern profilometry (FPP). As phase unwrapping errors are often caused by shadows on fringe patterns images and object surface discontinuities, we propose to identify both of them. An image of the object is acquired by averaging the fringe patterns reflected from the object surface, based on which shadow areas and the phase discontinuities are identified. When carrying out phase unwrapping, the shadow areas and the edge pixels will be isolated, and then the remaining areas are unwrapped by means of an efficient technique proposed below. All the remaining pixels are then unwrapped by an efficient technique proposed, by which, the computational efficiency can be significantly improved. As proven by the experiment results, the proposed approach is able to implement fast and reliable phase unwrapping. I. INTRODUCTIONFringe Pattern Profilometry (FPP) has been an active area of research due to its potential application in many noncontact 3D shape measurement scenarios [1-3]. A typical FPP system consists of a projector, a digital camera and a computer. In most applications, the digital projector generates a group of sinusoidal fringe patterns images which are projected onto the object surface to be measured [1]. The image patterns are distorted due to the height variance of surface shape which are captured by the camera. As the fringe patterns are sinusoidal, and the deformed patterns can be modeled as the result of phase modulation of the projected patterns. Consequently, the fringe patterns can be described by phase maps, and the 3D shape can be retrieved by analyzing the phase maps. However, the obtained phases are wrapped into the range ( , ] SS referred to wrapped phases. In order to obtain the 3D shapes, the true phases are required and must be recovered from the wrapped ones. Such a process is known as phase unwrapping which is a critical step in the FPP approaches.Many phase unwrapping algorithms have been proposed. Existing phase unwrapping algorithms can be classified into two categories: the temporal algorithms (such as gray-code method [2] and multi-frequency method [3]) and spatial algorithms including simple path-following method [6], branch-cut method [7], and quality-guided flood-fill (QGFF) method [14]. The temporal algorithms are able to accurately recover the true phases from complex wrapped phase maps. However they usually require extra fringe patterns and thus not suit for fast 3D acquisition. On the other hand, the spatial algorithms can be applied in the cases of less number of fringe patterns, but they have limited ability to unwrap phase maps with isolated regions and discontinuities.

show abstract

Section: The Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

An object image edge detection based quality-guided phase unwrapping approach for fast three-dimensional measurement

Chen

Song

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

View full text Add to dashboard Cite

show abstract

“…Assembly line product inspection etc.). Fringe pattern profilometry (FPP) is one of the most used technologies for 3D surface profile measurement because of its advantages of non-contact, high speed and high accuracy [1][2][3][4][5][6]. Fourier transform profilometry (FTP) and phase shifting profilometry (PSP) are the typical and popular methods implementing FPP.…”

Section: Introductionmentioning

confidence: 99%

Automated approach for the surface profile measurement of moving objects based on PSP

Lü¹,

Ding²,

Luan³

et al. 2017

Opt. Express

View full text Add to dashboard Cite

Phase shifting profilometry can achieve high accuracy for the 3D shape measurement of static object. Errors will be introduced when the object is moved during the movement. The fundamental reason causing the above issue is: PSP requires multiple fringe patterns but the reconstruction model does not include the object movement information. This paper proposes a new method to automatically measure the 3D shape of the rigid object with arbitrary 2D movement. Firstly, the object movement is tracked by the SIFT algorithm and the rotation matrix and translation vector describing the movement are estimated. Then, with the reconstruction model including movement information, a least-square algorithm is applied to retrieve the correct phase value. The proposed method can significantly reduce the errors caused by the object movement. The whole reconstruction process does not need human intervention and the proposed method has high potential to be applied in industrial applications. Experiments are presented to verify the effectiveness. Abstract: Phase shifting profilometry can achieve high accuracy for the 3D shape measurement of static object. Errors will be introduced when the object is moved during the movement. The fundamental reason causing the above issue is: PSP requires multiple fringe patterns but the reconstruction model does not include the object movement information. This paper proposes a new method to automatically measure the 3D shape of the rigid object with arbitrary 2D movement. Firstly, the object movement is tracked by the SIFT algorithm and the rotation matrix and translation vector describing the movement are estimated. Then, with the reconstruction model including movement information, a least-square algorithm is applied to retrieve the correct phase value. The proposed method can significantly reduce the errors caused by the object movement. The whole reconstruction process does not need human intervention and the proposed method has high potential to be applied in industrial applications. Experiments are presented to verify the effectiveness.

show abstract

“…At least three phaseshifting stripes can solve the primary phase. The primary phase can be revealed with the unwrapping algorithm to acquire the continuous phase [14] . The unwrapped phase value expresses the object height, and is also known as the range image.…”

mentioning

confidence: 99%

Registration and integration algorithm in structured light three-dimensional scanning based on scale-invariant feature matching of multi-source images

Cui¹,

Liao²,

Dai³

et al. 2012

中国光学快报

View full text Add to dashboard Cite

Based on the homography between a multi-source image and three-dimensional (3D) measurement points, this letter proposes a novel 3D registration and integration method based on scale-invariant feature matching. The matching relationships of two-dimensional (2D) texture gray images and two-and-a-halfdimensional (2.5D) range images are constructed using the scale-invariant feature transform algorithms. Then, at least three non-collinear 3D measurement points corresponding to image feature points are used to achieve a registration relationship accurately. According to the index of overlapping images and the local 3D border search method, multi-view registration data are rapidly and accurately integrated. Experimental results on real models demonstrate that the algorithm is robust and effective.OCIS codes: 100.0100, 120.0120, 120.2650, 150.0150. doi: 10.3788/COL201210.091001.Three-dimensional (3D) registration and integration are difficult but necessary steps in performing structured light 3D measurements [1] . A multiple-view measurement is needed to fully cover an object because of object occlusion, shadows or limited view depth, and so on. The overlapping layers of the same object must be integrated before the whole object is completely measured. The goal of registration is to determine the transform function of a view from one coordinate system to another [2−8] . Different methods of registration have been developed [2−8] , and can be divided into three categories. The first method involves the use of supporting hardware, such as turntable methods [2] , gyroscopic self-positioning devices [3] , etc. This method relies on precision supporting devices, and has a limited application range. The second method adds some additional features to the surface of the object and identifies the designed features to achieve registration. This method includes the paste landmark technique [4] and fixing the calibration plate on the object [5] , among others. However, one main drawback of this method is that the object may not be completely measured because of the obstruction caused by the additional feature. The third method is completely based on the 3D measurement data. This technique starts from an approximate registration, and iteratively reaches the final registration by minimizing an error functional, such as the iterative closest point algorithm [6] and various improved algorithms [7−8] . This method has the smallest measurement constraint and is most widely applied. However, its biggest drawback is the requirement of a good initial position; otherwise, convergence is not guaranteed [7−8] . To maximize the convenience of application, 3D measurement data and the related measurement process are used to simplify the complex problem. The matching scale-invariant features from two-dimensional (2D) gray images and two-and-a-half-dimensional (2.5D) range images are accurately recognized. The homographic relationship is then used to solve 3D registration and integration. The novelty of this approach is characterized by the fo...

show abstract

Reliability-guided phase-unwrapping algorithm for the measurement of discontinuous three-dimensional objects

Cited by 29 publications

References 18 publications

An object image edge detection based quality-guided phase unwrapping approach for fast three-dimensional measurement

An object image edge detection based quality-guided phase unwrapping approach for fast three-dimensional measurement

Automated approach for the surface profile measurement of moving objects based on PSP

Registration and integration algorithm in structured light three-dimensional scanning based on scale-invariant feature matching of multi-source images

Contact Info

Product

Resources

About