Computer Science ›› 2022, Vol. 49 ›› Issue (5): 25-32.doi: 10.11896/jsjkx.210600129

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sparse Point Cloud Filtering Algorithm Based on Mask

FENG Lei1,2, ZHU Deng-ming1, LI Zhao-xin1, WANG Zhao-qi1   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2 School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2021-06-16 Revised:2021-12-06 Online:2022-05-15 Published:2022-05-06
  • About author:FENG Lei,born in 1998,postgraduate.His main research interests include 3D reconstruction and so on.
    ZHU Deng-ming,born in 1973,Ph.D,associate researcher,master supervisor,is a member of China Computer Federation.His main research interests include virtual reality and human-computer interaction.
  • Supported by:
    National Key R & D Program of China(2020YFB1710400) and Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences(YJKYYQ20190055).

Abstract: Image-based 3D reconstruction is widely used in practice due to less hardware constraints,lower cost and higher flexibility.Especially for the problems of sparseness and uneven density of the three-dimensional point cloud data generated by the image due to the occlusion between various parts of the object,it has always been a difficulty and hot issue to deal with.In this paper,a mask-based sparse point cloud filtering algorithm is proposed.Firstly,the bounding box of the point cloud is calculated and the grid is adaptively divided according to the sparseness of the point cloud.Secondly,Depth-first search is used to recursively find all customized connected domains composed of grids generated at the first step.Then adaptively calculating the threshold based on the quantized importance index,selecting the connected domains that should be retained based on the adaptive threshold,and defining the set of all retained connected domains as a mask,which is used to describe the global spatial topology information of the sparse point cloud.Finally,points covered by the mask are retained while points of the uncovered area are removed,so as to filter the outliers.This method can handle the point cloud data generated by occlusion and with great differences in spatial density.It can effectively remove outliers in the original three-dimensional point cloud data,while maintaining the detailed information of the point cloud.

Key words: 3D reconstruction, Mask, Point cloud filtering, Stereomatching, Triangular mesh processing

CLC Number: 

  • G312
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