Denoising of 3D Point Clouds Constructed from Light Fields

Galea, Christian; Guillemot, Christine

doi:10.1109/icassp.2019.8683548

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…These problems can be traced back to the general solution of the ICP formulation, which is based on a leastsquare minimization method. Special filters such as outlier removal [39] or de-noising [40] provide an improvement; however, in some cases, ICP still leads to unreliable fitting results, especially for complex shapes such as curved surfaces [41].…”

Section: Sensors and Methods For The Determination Of Pose And Geomet...mentioning

confidence: 99%

Assembly of Compliant Structures with Autonomous Industrial Mobile Manipulators (AIMM) Using an End Effector with Active Deformation Compensation for the Assembly of Flaps

et al. 2022

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Composite structures in aeroplanes are often thin-walled and lightweight, resulting in significant compliance, which presents a handling and assembly challenge due to the associated part deformations. In order to counteract these deformations, the parts are held in their specified geometry using stiff and correspondingly heavy fixtures or jigs. Mobile industrial robots are very versatile and widely used in industrial volume production, but they are limited in their payload capacity. High-rate production of large aerospace modules requires highly automated flexible assembly processes. The approach presented in this paper is to combine mobile units with lightweight assembly jigs that have the capability of deformation compensation. The subject of the study is a high-rate assembly process for flap modules using an Autonomous Industrial Mobile Manipulator (AIMM) and a lightweight end effector. The end effector has a shape compensation function, implemented by an integrated Stewart platform, which enables the compensation of manufacturing tolerances as well as gravity effects. The compensation function is used in a closed loop and counteracts shape deviations by appropriate fixture shape adjustments. The paper reports on the conceptual design of the assembly scenario, the design of the end effector, its realization and the successful experimental demonstration at 1:1 scale.

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Section: Sensors and Methods For The Determination Of Pose And Geomet...mentioning

confidence: 99%

Assembly of Compliant Structures with Autonomous Industrial Mobile Manipulators (AIMM) Using an End Effector with Active Deformation Compensation for the Assembly of Flaps

et al. 2022

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“…In order to improve the accuracy of 3D point cloud, Christian et.al. [4] proposed a light-field based 3D point cloud denoising method by using geometric and photometric properties and known camera parameters to correct the location of outliers. Leal et.al.…”

Section: Introductionmentioning

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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“…To improve the accuracy of 3D point clouds, various researchers have proposed point cloud denoising methods. Galea et al [18] introduced a light field-based approach using uncertainty measures and geometric and photometric properties to estimate depth values and correct outliers. This method suffers from inaccuracies in correcting point locations for complex light field data, which introduce errors in the point cloud during denoising.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Long

et al. 2023

Electronics

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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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Denoising of 3D Point Clouds Constructed from Light Fields

Cited by 10 publications

References 12 publications

Assembly of Compliant Structures with Autonomous Industrial Mobile Manipulators (AIMM) Using an End Effector with Active Deformation Compensation for the Assembly of Flaps

Assembly of Compliant Structures with Autonomous Industrial Mobile Manipulators (AIMM) Using an End Effector with Active Deformation Compensation for the Assembly of Flaps

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

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

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