Objective

2

Digital restoration of historical cultural relics

simulation of medical equipment and human organs

and virtual teaching reflect that 3D reconstruction technology plays an extremely important role in the development level of human society. However

in 3Dreconstruction

geometric data in the RGB-D (RGB Depth) data collected by a depth camera have low quality and high noise. In 3D point cloud registration

the features of geometric data obtained from such low-quality and high-noise data are the only means to obtain an accurate registration result of the point cloud. However

the point cloud registration effect is not particularly obvious. Given that point cloud noise interference is relatively large and geometric information is unclear

which has a non-negligible effect on the registration accuracy. Color information is introduced to increase the feature information of the model. However

when the model data are collected by as canning device

the color is greatly affected by a change in illumination

and the model surface is affected by changes in illumination intensity and angle of view. These effects lead to an obvious color change and inevitable interference. In this study

an RGB-D point cloud registration method based on illumination compensation is proposed that considers 3D point cloud color information as susceptible to illumination conditions.

Method

2

The color data of the model surface consist of the product of illumination and reflection components. The illuminance component varies with spatial position and displays low-frequency information

where as the reflected component has various texture details visible to the human eye and expresses high-frequency information. The brightness of low-frequency information of color is suppressed when combined with homomorphic filtering

the amount of high-frequency information is enhanced

the influence of color changes by illumination is eliminated

and the consistency of color information is improved. RGB-D data acquired by a Kinect device must be sorted to form a linear point sequence. The color and geometric features of the model are acquired and weighted into a mixed feature to define the feature points of the source point cloud

and the K-nearest neighbor algorithm is used to search for the corresponding points. The singular value decomposition algorithm is used to obtain a rigid transformation matrix.

Result

2

Through experiments

data are collected from four models at different light intensities using Kinect scanning equipment. The experimental categories comprise three sets

namely

strong light and low light

left light and right light

and strong light and side light. Our method is compared with the traditional iterative closest point (ICP)

super four-point congruent set(Super 4PCS)

4D ICP (hue + ICP) combined with hue and geometry information

and blending of geometric and color information. In the course of the experiments

the iteration running time

number of iterations

and average distance error of scale-invariant feature transform (SIFT) feature points are used as the evaluation criteria for the registration experiment results. The effects of different light intensity models after registration by each algorithm are also shown for comparison. Results according to the traditional ICP algorithm

the combination of depth information and hue

and the model registration experiment of different light intensity combinations on the David model with uniform mesh surface show that the times are relatively flat; the average running times are 8.920 s

8.796 s

and 5.191 s; and the average errors of feature point matching are 3.677

3.102

and 1.029

respectively. Registration time and average error of feature point matching are improved by approximately 50%. On the Archimedes model with inconsistent mesh surface irregularities

the number of iterations is undistinguishable. The average running times of the registration are 6.926 s

3.955 s and 3.853 s

and the average errors of feature point matching are 26.718

27.653

and 28.843

respectively. On the barrel model with smooth mesh surface

the average running times of registration are 0.509 s

1.937 s and 0.691 s

and the average errors of feature point matching are 23.830

22.820

and 3.931

respectively. On the barrel model with smooth mesh surface and the Archimedes model with inconsistent mesh surface irregularities

the average error of feature point matching is reduced to one-sixth and one-eighth of the comparison method. This study also compares the Super 4PCS and color point cloud registration algorithms under different combined illumination intensities. Of the four mesh structure models

the average distance error of SIFT feature points of the proposed algorithm is the lowest. The full-distance reduction is reduced to one-fifth of the comparison method

which proves that the proposed algorithm has stability in the model registration of different illumination intensities. This study further conducts correlation experiments on the missing point cloud model and proves that the algorithm can deal with the problem of point cloud registration with missing points. When the overlap rate of two point clouds is approximately 40%

the algorithm registration in this study can achieve better results.

Conclusion

2

Homomorphic filtering algorithm is used to suppress the influence of illumination changes

and the consistency of color information is improved. The interference of uneven illumination intensity on 3D point cloud registration accuracy is eliminated. The registration method is verified by comparing it with other correlation registration algorithms. The method has obvious advantages in algorithm stability and registration accuracy.