Automatic reconstruction method for large scene based on multi-site point cloud stitching

Xu, Haonan; Yu, Lei; Hou, Junyi; Fei, Shumin

doi:10.1016/j.measurement.2018.09.022

Cited by 44 publications

(19 citation statements)

References 22 publications

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“…For the coordinate extraction of the mark points, the image processing processes such as graying, gaussian smoothing, thresholding, canny edge extraction, and ellipse centroid extraction are carried out for the blade image taken at different positions. After matching, the coordinates of the mark points are obtained according to the point cloud extraction method, and then through the point cloud splicing based on the mark points, all the measurement data are unified in the same coordinate system, realizing full-visual three-dimensional splicing to obtain the blade point cloud data [21], [22].…”

Section: Acquisition Of the Model To Be Repaired A Point Cloud Acquisition Methods Based On Speckle Measurementmentioning

confidence: 99%

Adaptive Positioning Repair Method for Aero-Engine Blades by Using Speckle Vision Measurement

Zhan

Zhang

et al. 2020

IEEE Access

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The repair of damaged aero-engine blades has an extremely high economic value. Due to the complexity of the blade surface and various kinds of deformation during the lifetime, blade positioning in the repair process has always been one of the key problems in the aviation industry. In this study, a method of blade self-adaptive positioning repair method based on speckle vision measurement is proposed. Firstly, a speckle vision measurement system is established to measure the speckles coated on the damaged blade surface to obtain its surface point cloud. Meanwhile, the coordinates of the reference points on the fixture are obtained to build the relative pose model between the blade and the fixture. Secondly, through the solution of posture relations among the mark point coordinate system, measuring camera coordinate system, probe coordinate system, and machine coordinate system, the self-adaptive positioning of the underpositioning clamping blade in the machine coordinate system is achieved. Then based on the positioning model, the machining repair parameters are selected reasonably to complete the CNC (Computer numerical control) path planning of tool repair. Finally, the DMU five-axis numerical control center was used to carry out adaptive positioning repair experiments on the damaged blades. The experimental results showed that this method could achieve satisfying repair effect in under positioning clamping, and provide a new method for repairing damaged aero-engine blades.INDEX TERMS Repair for damaged blades, speckle vision measurement, adaptive positioning, CNC repair path planning.

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Section: Acquisition Of the Model To Be Repaired A Point Cloud Acquisition Methods Based On Speckle Measurementmentioning

confidence: 99%

Adaptive Positioning Repair Method for Aero-Engine Blades by Using Speckle Vision Measurement

Zhan

Zhang

et al. 2020

IEEE Access

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“…In this paper, the switching system scheme is introduced into the filter for the first time, and applied to the large-screen interactive imaging system. A switching system consists of a series of sequential or discrete differential equations subsystems and switching rules or a switching strategy [4,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. If the entire filtering process is regarded as a hybrid dynamic system, then each filter algorithm can be regarded as a subsystem of the system.…”

Section: Dynamic Gesture Tracking Algorithm With Switching Federated mentioning

confidence: 99%

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Hou

2018

Complexity

Self Cite

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Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.

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“…In recent years, as an important 3D data representation, point clouds have received more attention and has been gradually applied to many fields such as autonomous driving [6,10,30,33,36,48,59], robotics [5,14,18,19,29,40,60] and virtual reality [2,4,43,52]. Generally, the raw point clouds obtained by 3D sensors are usually sparse.…”

Section: Introductionmentioning

confidence: 99%

SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering

Zhao¹,

Hui²,

Xie³

2021

Preprint

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Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. In this paper, we propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth. To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images. Specifically, we first propose a neighbor expansion unit (NEU) to upsample the sparse point clouds, where the local geometric structures of the sparse point clouds are exploited to learn weights for point interpolation. Then, we develop a differentiable point cloud rendering unit (DRU) as an end-to-end module in our network to render the point cloud into multi-view images. Finally, we formulate a shape-consistent loss and an imageconsistent loss to train the network so that the shapes of the sparse and dense point clouds are as consistent as possible. Extensive results on the CAD and scanned datasets demonstrate that our method can achieve impressive results in a self-supervised manner.

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Automatic reconstruction method for large scene based on multi-site point cloud stitching

Cited by 44 publications

References 22 publications

Adaptive Positioning Repair Method for Aero-Engine Blades by Using Speckle Vision Measurement

Adaptive Positioning Repair Method for Aero-Engine Blades by Using Speckle Vision Measurement

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering

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