scholarly journals Color Point Cloud Registration Algorithm Based on Hue

2021 ◽  
Vol 11 (12) ◽  
pp. 5431
Author(s):  
Siyu Ren ◽  
Xiaodong Chen ◽  
Huaiyu Cai ◽  
Yi Wang ◽  
Haitao Liang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Error Function ◽  
Point Clouds ◽  
Tangent Plane ◽  
Geometric Error ◽  
Color Information ◽  
Geometric Information ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Lighting Conditions

ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, color information of point cloud, such as gray, varies differently under different lighting conditions. Using gray as the color information to register can cause large errors and even wrong results. To solve this problem, we propose a color point cloud registration algorithm based on hue, which has good robustness at different lighting conditions. We extract the hue component according to the color information of point clouds and make the hue distribution of the tangent plane continuous. The error function consists of color and geometric error of two point clouds under the current transformation. We optimize the error function using the Gauss–Newton method. If the value of the error function is less than the preset threshold or the maximum number of iterations is reached, the current transformation relationship is required. We use RGB-D Scenes V2 dataset to evaluate our algorithm and the results show that the average recall of our algorithms is 8.63% higher than that of some excellent algorithms, and its RMSE of 14.3% is lower than that of the other compared algorithms.

scholarly journals Hue-assisted automatic registration of color point clouds

2014 ◽  
Vol 1 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Hao Men ◽  
Kishore Pochiraju
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Local Geometry ◽  
Automatic Registration ◽  
Color Information ◽  
Registration Accuracy ◽  
Surface Color ◽  
Registration Algorithm ◽  
Sample Density ◽  
Occupancy Grids

Abstract This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the registration speed and improves the registration accuracy. Coarse rigid transformations determined in this step enable fine alignment with dense, unfiltered point clouds or using Iterative Common Point (ICP) alignment techniques.

scholarly journals TOWARDS RADIOMETRICAL ALIGNMENT OF 3D POINT CLOUDS

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 419-424 ◽  
Author(s):  
H. A. Lauterbach ◽  
D. Borrmann ◽  
A. Nüchter
Keyword(s):  
Point Cloud ◽  
Dynamic Range ◽  
Point Clouds ◽  
Color Information ◽  
Lighting Conditions ◽  
3D Point Clouds ◽  
Laser Scanners ◽  
3D Scans ◽  
Colored Point

3D laser scanners are typically not able to collect color information. Therefore coloring is often done by projecting photos of an additional camera to the 3D scans. The capturing process is time consuming and therefore prone to changes in the environment. The appearance of the colored point cloud is mainly effected by changes of lighting conditions and corresponding camera settings. In case of panorama images these exposure variations are typically corrected by radiometrical aligning the input images to each other. In this paper we adopt existing methods for panorama optimization in order to correct the coloring of point clouds. Therefore corresponding pixels from overlapping images are selected by using geometrically closest points of the registered 3D scans and their neighboring pixels in the images. The dynamic range of images in raw format allows for correction of large exposure differences. Two experiments demonstrate the abilities of the approach.

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Point Cloud Registration Algorithm Based on Extended Point Feature Histogram Feature

2019 ◽  
Vol 56 (24) ◽  
pp. 241503
Author(s):  
汤慧 Tang Hui ◽  
周明全 Zhou Mingquan ◽  
耿国华 Geng Guohua
Keyword(s):  
Point Cloud ◽  
Point Cloud Registration ◽  
Registration Algorithm ◽  
Histogram Feature ◽  
Extended Point ◽  
Point Feature

scholarly journals 3-D Point Cloud Registration Using Convolutional Neural Networks

2019 ◽  
Vol 9 (16) ◽  
pp. 3273 ◽  
Author(s):  
Wen-Chung Chang ◽  
Van-Toan Pham
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Full Range ◽  
Point Clouds ◽  
Fixed Time ◽  
Training Data ◽  
Data Sets ◽  
Point Cloud Registration ◽  
Cloud Data ◽  
Model Point

This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

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