Sparse Point Cloud Filtering Based on Covariance Features

Abstract

<p><strong>Abstract.</strong> This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.</p>

Validation of a Uav-Derived Point Cloud by Semantic Classification and Comparison With TLS Data

“…The choice can be based on a-priori definition of the search area in terms of radius or number of points (Arya et al, 1998;Friedman et al, 1977), or adapting this parameter according with the local geometry of the point cloud (Farella et al, 2019;Martin Weinmann et al, 2015). While the former requires an empiric knowledge of the scene, the latter is more versatile because it is not restricted to a specific dataset.…”

“…These values, obtained from the covariance matrix, represent the variation of the points distribution along the three principal orthogonal directions. Eigenvalues can be combined to obtain some shape descriptors called eigenfeatures (Farella et al, 2019;M. Weinmann et al, 2015a) which enclose: linearity , planarity , scattering , omnivariance , anisotropy , eigentropy , sum of eigenvalues and change of curvature ; Table 1 reports theirs mathematical formulation.…”

Improving Performance of Feature Extraction in SFM Algorithms for 3d Sparse Point Cloud

“…Consequently, the quality assessment of point clouds is a challenging problem since this 3D representation format is unstructured (Javaheri et al, 2017). To solve this issue, different approaches have been proposed in previous studies: outliers filtering (Hu et al, 2019) and noise smoothing (Wang et al, 2013); automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase (Farella et al, 2019). These studies have focused on finding automatic solutions to the problem.…”

“…The result of point clouds is also affected by bad illumination conditions (Girardeau-Montaut et al, 2005), the thickness of the object and its transparency. Above all, the way in which the data is acquired can cause noisy results and blunders especially when different platforms or lowcost sensors (Byrne et al, 2017) are employed, due to scale and illumination changes or quality and quantity of single sources (Farella et al, 2019).…”

Multi-Scalar Surveys for Complex Architectures

“…Inserting them one at a time, the clouds were automatically overlapped by the programme, thus confirming the correct referencing of the data. To further reduce programme processing times, it was decided to eliminate any noise elements from the laser scanning cloud that were deemed unsuitable and unnecessary for the comparison (Farella et al, 2019). Through the Cloudcompare software, the distance calculation can be carried out using two different methods: cloud-cloud distances and cloud-mesh distances.…”