Density-Based Denoising of Point Cloud

Zaman, Faisal; Wong, Ya Ping; Ng, Boon Yian

doi:10.1007/978-981-10-1721-6_31

Cited by 39 publications

(22 citation statements)

References 15 publications

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“…It eliminates sparse outliers employing the relative deviation of the local neighborhood and takes a clustering-based approach to remove small clusters of outliers. Another successful filter for removing outliers and noises and preserving features is described in [24], in which a density-based method is applied. A particle swarm optimization approach is adopted for the approximation of the optimal bandwidth for kernel density to fulfill the robustness.…”

Section: Related Workmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning.

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Section: Related Workmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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“…Methods based on KDE have been successfully applied in denoising of noisy point-clouds [9,63], clustering (of noisy point-cloud data) [17] and different types of outlier detection [33,36], which are all based on separating salient elements from non-salient elements based on density.…”

Section: Kernel Density and Mass Estimationmentioning

confidence: 99%

Stochastic Distance Transform: Theory, Algorithms and Applications

2020

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Distance transforms (DTs) are standard tools in image analysis, with applications in image registration and segmentation. The DT is based on extremal (minimal) distance values and is therefore highly sensitive to noise. We present a stochastic distance transform (SDT) based on discrete random sets, in which a model of element-wise probability is utilized and the SDT is computed as the first moment of the distance distribution to the random set. We present two methods for computing the SDT and analyze them w.r.t. accuracy and complexity. Further, we propose a method, utilizing kernel density estimation, for estimating probability functions and associated random sets to use with the SDT. We evaluate the accuracy of the SDT and the proposed framework on images of thin line structures and disks corrupted by salt and pepper noise and observe excellent performance. We also insert the SDT into a segmentation framework and apply it to overlapping objects, where it provides substantially improved performance over previous methods. Finally, we evaluate the SDT and observe very good performance, on simulated images from localization microscopy, a state-of-the-art super-resolution microscopy technique which yields highly spatially localized but noisy point-clouds.

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“…This could reduce the effectiveness of the filtering procedure, the preservation of the topology, and the capacity to identify outliers. For these reasons, further methods and hybrid techniques (Zaman et al, 2016) are nowadays considered more interesting approaches for noise removal while trying to preserve objects shape and properties. When the filtering procedure is mainly based on the analysis of geometric properties, the covariance matrix can be used as a shape descriptor of the point cloud (Xiao et al, 2006;Pauly et al, 2002).…”

Section: Related Workmentioning

confidence: 99%

Sparse Point Cloud Filtering Based on Covariance Features

Farella

Torresani

Remondino

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<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>

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Density-Based Denoising of Point Cloud

Cited by 39 publications

References 15 publications

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Stochastic Distance Transform: Theory, Algorithms and Applications

Sparse Point Cloud Filtering Based on Covariance Features

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