An efficient outlier removal method for scattered point cloud data.

Xiaojuan Ning,Yinghui Wang,Fan Li,Ge Tian,Miguel A Fernandez

doi:10.1371/journal.pone.0201280

Abstract

Outlier removal is a fundamental data processing task to ensure the quality of scanned point cloud data (PCD), which is becoming increasing important in industrial applications and reverse engineering. Acquired scanned PCD is usually noisy, sparse and temporarily incoherent. Thus the processing of scanned data is typically an ill-posed problem. In the paper, we present a simple and effective method based on two geometrical characteristics constraints to trim the noisy points. One of the geometrical characteristics is the local density information and another is the deviation from the local fitting plane. The local density based method provides a preprocessing step, which could remove those sparse outlier and isolated outlier. The non-isolated outlier removal in this paper depends on a local projection method, which placing those points onto objects. There is no doubt that the deviation of any point from the local fitting plane should be a criterion to reduce the noisy points. The experimental results demonstrate the ability to remove the noisy point from various man-made objects consisting of complex outlier.

Highlights

Scanning object with complex geometry and varying surface reflectiveness, the collected scanned point cloud may contain extensive outliers, which are inevitable by-products of 3D scanning [1,2,3]
We proposed an automatic method to remove those outliers based on local density and local projection
Different from other denoising methods, the procedure does not remove noisy point but project noisy point onto the local fitting plane to make the model more regular

Summary

Introduction

Scanning object with complex geometry and varying surface reflectiveness, the collected scanned point cloud may contain extensive outliers, which are inevitable by-products of 3D scanning [1,2,3]. Usually unorganized, noisy, sparse, and inconsistent in local point density, have geometrical discontinuities, arbitrary surface shape with sharp features [4]. Sparse and dense outliers pose much more problematic issues to the applications of the scanned point cloud, especially in 3D shape analysis [5], object modeling [6] and object recognition [7]. We proposed an automatic method to remove those outliers based on local density and local projection. The two types of noise both can be detected and removed by the method based on local density. The non-isolated outlier is close to the model, we proposed to project those outliers locally onto the original object through the local fitting plane. Different from other denoising methods, the procedure does not remove noisy point but project noisy point onto the local fitting plane to make the model more regular. We can obtain the noisefree model through the two methods and prove that our method is effective to denoise the point cloud model

Related work

2: Output: sparse outlier and isolated outlier removal results

1: Input: Three dimensional scanned point cloud data with various outliers

Experimental results

Limitation

Conclusion