Abstract
Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which is an obstacle for real-time applications that do not rely on point cloud post-processing. In order to fill this gap, by analyzing the noise region based on position and shape, we proposed a composite filtering system for using consumer depth cameras at close range. The system consists of three main modules that are used to eliminate different types of noise areas. Taking the human hand depth image as an example, the proposed filtering system can eliminate most of the noise areas. All algorithms in the system are not based on window smoothing and are accelerated by the GPU. By using Kinect v2 and SR300, a large number of contrast experiments show that the system can get good results and has extremely high real-time performance, which can be used as a pre-step for real-time human-computer interaction, real-time 3D reconstruction, and further filtering.
Highlights
The reasons for the success of consumer depth cameras are low price, acceptable accuracy, lower learning costs, extensive applicability, and excellent portability
The range could be changed by using Draelos’s method [3], according to our observation, when a target surface gets close to the nearest limit, for example, 3D reconstruction of small objects [1], in the point cloud acquired by consumer depth cameras, there will be some irregular shape of noise areas surrounding or on the edge of the realistic surface
NNZP (δ(p)) < k, when p locates at the jagged edges, p is defined as a noise point
Summary
Yuanxing Dai 1, *, Yanming Fu 2 , Baichun Li 3 , Xuewei Zhang 1 , Tianbiao Yu 1 and Wanshan Wang 1. College of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300000, China
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