Abstract
A point cloud that is obtained by an RGB-D camera will inevitably be affected by outliers that do not belong to the surface of the object, which is due to the different viewing angles, light intensities, and reflective characteristics of the object surface and the limitations of the sensors. An effective and fast outlier removal method based on RGB-D information is proposed in this paper. This method aligns the color image to the depth image, and the color mapping image is converted to an HSV image. Then, the optimal segmentation threshold of the V image that is calculated by using the Otsu algorithm is applied to segment the color mapping image into a binary image, which is used to extract the valid point cloud from the original point cloud with outliers. The robustness of the proposed method to the noise types, light intensity and contrast is evaluated by using several experiments; additionally, the method is compared with other filtering methods and applied to independently developed foot scanning equipment. The experimental results show that the proposed method can remove all type of outliers quickly and effectively.
Highlights
The 3D point cloud, due to its simplicity, flexibility and powerful representation capability, has become a new primitive representation for objects and has attracted extensive attention in many research fields, such as reverse engineering, 3D printing, archaeology, virtual reality, medicine and other fields [1,2,3,4,5]
It can be clearly seen from the original point cloud with color that the isolated outliers are mainly included in View 1 and View 3, while the non-isolated outliers are mainly included in View 2 and View 4
It was found from the removed point cloud with color that some valid points were removed by mistake, which are mainly concentrated near the contact surface of the object and the platform because of the small contrast on the contact surface
Summary
The 3D point cloud, due to its simplicity, flexibility and powerful representation capability, has become a new primitive representation for objects and has attracted extensive attention in many research fields, such as reverse engineering, 3D printing, archaeology, virtual reality, medicine and other fields [1,2,3,4,5]. The manipulation of the point cloud can have better performance and lower overhead. These remarkable advantages make the research on manipulating point clouds become a hot topic.
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