Absolute phase map recovery of two fringe patterns with flexible selection of fringe wavelengths

Long, Jiale; Xi, Jiangtao; Zhu, Ming; Cheng, Wenqing; Cheng, Rui; Li, Zhongwei; Shi, Yusheng

doi:10.1364/ao.53.001794

Cited by 23 publications

(5 citation statements)

References 18 publications

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“…A duck doll is selected as the experimental object, as shown in Figure 3(a), one of the deformed fringe images captured by the camera is shown in Figure 3(b). According to the three-dimensional topography measurement principle based on dual-wavelength fringe projection [15] , the absolute phase map is obtained by analyzing and phase unwrapping multiple deformed fringe patterns by PSP, as shown in Figure 3(c). Combining the 3D calibration parameters and the absolute phase map of the object for 3D reconstruction, the reconstructed 3D point cloud is shown in Figure 3(d).…”

Section: Resultsmentioning

confidence: 99%

3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

Long,

Du,

Zhang

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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In processing the point cloud of three-dimensional (3D) reconstruction, various point cloud noises would generate, resulting in decreasing the accuracy of 3D point cloud. In order to improve the accuracy of 3D point cloud, this paper proposes a point cloud denoising method based on 3D point cloud segmentation. This method can be divided into four steps. Firstly, the K Nearest Neighbor (KNN) algorithm is used to construct a KNN look-up table for 3D point cloud. Secondly, the look-up table is used to segment the 3D point cloud to obtain the noise point cloud and the noise-free point cloud. Then, using the relationship between the noise point cloud and the noise point of the absolute phase, the reference noise-free phase is established to restore the absolute phase of the noise-free point. Finally, 3D reconstruction is performed according to the recovered absolute phase to obtain a noise-free 3D point cloud. This proposed method only calculates the KNN once for the initial 3D point cloud and only uses a KNN look-up table to segment the point cloud, which also accelerates the speed of 3D point cloud segmentation. The experimental results show that this method not only removes the noisy point cloud, but also restores part of the noisy point cloud into a noise-free 3D point cloud, improving the accuracy of the 3D point cloud.

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Section: Resultsmentioning

confidence: 99%

3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

Long,

Du,

Zhang

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

View full text Add to dashboard Cite

show abstract

“…Im is the imaginary part of the complex number, In is the natural logarithm, and R f * (x) represents the complex conjugate of R f (x). There is a corresponding relationship between Φ (x) and u p in the projected fringe image coordinates [33]:…”

Section: D Imaging Calculationmentioning

confidence: 99%

Simulation study of compressed ultrafast 3D imaging based on interferometry

Long,

Du,

Zhang

et al. 2024

Meas. Sci. Technol.

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Observing the 3D (three-dimensional) surface vibration in hundred MHz is challenging due to low speed of CCD/CMOS camera. In order to obtain the ultrafast dynamic 3D shape information, a compressed ultrafast 3D imaging framework based on interferometry is proposed in this paper. Based on this imaging framework, an reconstruction method combining Plug and Play-Fast Flexible Denoising Network (PNP-FFDNET) is developed to recover high resolution interference fringe patterns and the corresponding 3D information. The simulation results show that the proposed method can observe the dynamic processes of the anti-phase vibration and the in-phase vibration of the crystal oscillator, therefore the dynamic 3D model is established. The proposed method outperforms other reconstruction methods in terms of average Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) in recovery the ultrafast dynamic 3D surface.

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“…With the development of image processing technology, sensor technology, and optical technology, 3D imaging technology is widely used in various fields [2,3]. At present, the main methods involved in non-contact 3D imaging technology are the binocular stereo vision method [4,5], the photometric stereo method [6,7], structured light 3D imaging [8][9][10], etc. The binocular stereo vision method [11] is the principle of binocular visual observation by simulating human beings.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Point Cloud-Filtering Method Based on Image Segmentation and Absolute Phase Recovery

Zhang

Long

et al. 2023

Electronics

Self Cite

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In three-dimensional (3D) shape measurement based on fringe projection, various factors can degrade the quality of the point cloud. Existing point cloud filtering methods involve analyzing the geometric relationship between 3D space and point cloud, which poses challenges such as complex calculation and low efficiency. To improve the accuracy and speed of point cloud filtering, this paper proposes a new point cloud filtering method based on image segmentation and the absolute phase for the 3D imaging obtained by fringe projection. Firstly, a two-dimensional (2D) point cloud mapping image is established based on the 3D point cloud obtained from fringe projection. Secondly, threshold segmentation and region growing methods are used to segment the 2D point cloud mapping image, followed by recording and removal of the segmented noise region. Using the relationship between the noise point cloud and the absolute phase noise point in fringe projection, a reference noise-free point is established, and the absolute phase line segment is restored to obtain the absolute phase of the noise-free point. Finally, a new 2D point cloud mapping image is reconstructed in 3D space to obtain a point cloud with noise removed. Experimental results show that the point cloud denoising accuracy calculated by this method can reach up to 99.974%, and the running time is 0.954 s. The proposed method can effectively remove point cloud noise and avoid complex calculations in 3D space. This method can not only remove the noise of the 3D point cloud but also can restore the partly removed noise point cloud into a noise-free 3D point cloud, which can improve the accuracy of the 3D point cloud.

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Absolute phase map recovery of two fringe patterns with flexible selection of fringe wavelengths

Cited by 23 publications

References 18 publications

3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

3D point cloud denoising method based on K nearest neighbor and absolute phase recovery

Simulation study of compressed ultrafast 3D imaging based on interferometry

Three-Dimensional Point Cloud-Filtering Method Based on Image Segmentation and Absolute Phase Recovery

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