Robust normal vector estimation in 3D point clouds through iterative principal component analysis

Sanchez, Julia; Denis, Florence; Cœurjolly, David; Dupont, Florent; Trassoudaine, Laurent; Checchin, Paul

doi:10.1016/j.isprsjprs.2020.02.018

Cited by 57 publications

(41 citation statements)

References 47 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PCA first introduces an orthogonal transformation, which converts the component-related random point cloud vector into a new random point cloud feature vector with uncorrelated components. The mutual influence between the neighboring points in the point cloud data can be eliminated, thus yielding a more accurate result in the subsequent normal vector estimation [25]. The normal vector estimation of point cloud data involves estimating the principal component that retains most of the point cloud information as a feature vector [26].…”

Section: A Improved Principal Component Analysis Methods For Point Cloud Normal Vector Estimationmentioning

confidence: 99%

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Ren

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The multiscale noise in the 3D point cloud data of rock surfaces which collected by 3D scanners has a significant influence on the exploration of rock surface morphology. To this end, this paper proposes a multiscale noise removal overall filtering algorithm. The specific processing procedure of the algorithm is as follows. First, a weighted principal component analysis is performed on point cloud data, i.e., the neighboring point distance is used as a weight in the principal component analysis, the covariance feature matrix of the weighted point is estimated, and the eigenvector corresponding to the lowest eigenvalue is used as the normal vector of the point cloud data. Second, in the weighted principal component analysis, estimating three eigenvalues corresponding to the Eigen matrix of the point cloud data, the ratio of the eigenvalue corresponding to the normal vector to the sum of three eigenvalues is used as the surface change factor. For the sample point, if the surface change factor of one sample point is less than the average value of the surface change factor of all sample points in the neighborhood, the sample point belongs to a flat area; otherwise, it belongs to a mutation area. Finally, in order to achieve multiscale noise removal, statistical filtering algorithm is used to remove large scale noise in flat area, additionally bilateral filtering algorithm is adopted to remove small scale noise in mutation area. In the experiments, the improved principal component analysis is combined with the overall filtering algorithm to accurately estimate the eigenvalues of the point cloud data points. After that, the eigenvalues of the sample points are used to distinguish between flat area and mutation area, so as to consider large scale noise and small scale noise. From the experimental results, it can be seen that overall filtering algorithm can consider both large scale and small scale noise and can remove noise from the point cloud data of rock samples. Visual judgment, normal distribution and fractal distribution tests are employed on filtered rock sample point cloud data to verify the reliability of the filtering results.INDEX TERMS 3D scanner, point cloud data, multiscale noise, statistical filtering, bilateral filtering.

show abstract

Section: A Improved Principal Component Analysis Methods For Point Cloud Normal Vector Estimationmentioning

confidence: 99%

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Ren

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…While these are intrinsically given for the individual triangles comprising a triangle mesh, the individual points of indoor mapping point clouds do not generally have normal vectors. These can however be easily determined by means of established methods such as in [85][86][87][88], which we assumed in this work as a necessary preprocessing step. Note that these normal vectors need not be oriented, i.e., pointing consistently towards the inside or outside of the building.…”

Section: Methodsmentioning

confidence: 99%

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

et al. 2021

View full text Add to dashboard Cite

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries was determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public.

show abstract

“…The normal vectors of points in a point cloud are important geometric properties that have been widely used by many authors to find the fold and boundary points, and high-quality surfaces [17,21,24,36,37]. Although there are several methods for estimating normal vectors in a point cloud, they are mainly proposed for 3D geometric models that have less noise with high point densities and most of the models contain smooth surfaces.…”

Section: Normal Vector Calculationmentioning

confidence: 99%

“…Dey et al [43] proposed an improved approach and solved the limitations of the MCMD to construct more accurate planes. Recently, Sanchez et al [24] proposed a robust normal estimation technique through an iterative weighted PCA [39] and the robust statistical M-estimators [44]. In the weighted PCA, the neighbouring points are assigned different weights based on their distance to P i .…”

Section: Normal Vector Calculationmentioning

confidence: 99%

“…To measure the directionality and the geometric changes, the estimation of normal and curvature for each 3D point in the data is an important factor and should be calculated accurately [22]. However, estimation of the normal vector along a building edge highly depends on the neighbourhood employed for each point [21,23], and the inharmonious nature of LiDAR point cloud makes the calculation of that neighbourhood complex and challenging [22,24]. Moreover, noise associated in the oblique point cloud data can create serious problems for calculating accurate normals in the context of affective and automatic building plane reconstruction [25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

et al. 2021

View full text Add to dashboard Cite

Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.

show abstract

Robust normal vector estimation in 3D point clouds through iterative principal component analysis

Cited by 57 publications

References 47 publications

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Overall Filtering Algorithm for Multiscale Noise Removal From Point Cloud Data

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data

Contact Info

Product

Resources

About