A review of algorithms for filtering the 3D point cloud
TL;DR: This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud, categorized into seven classes, which concentrate on their common and obvious traits.
Abstract: In recent years, 3D point cloud has gained increasing attention as a new representation for objects However, the raw point cloud is often noisy and contains outliers Therefore, it is crucial to remove the noise and outliers from the point cloud while preserving the features, in particular, its fine details This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud The existing methods are categorized into seven classes, which concentrate on their common and obvious traits An experimental evaluation is also performed to demonstrate robustness, effectiveness and computational efficiency of several methods used widely in practice
Citations
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TL;DR: This work develops a simple data‐driven method for removing outliers and reducing noise in unordered point clouds using a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds.
Abstract: Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g. jets or MLS surfaces), local or non-local averaging or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely sampled point clouds. In our extensive evaluation, both on synthetic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline. Both the code and pre-trained networks can be found on the project page (https://github.com/mrakotosaon/pointcleannet).
186 citations
TL;DR: This paper extends a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seeks self-similar patches to denoise them simultaneously using the patch manifold prior, and proposes a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
Abstract: 3D point cloud—a new signal representation of volumetric objects—is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud—e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors—imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges.
121 citations
Cites background from "A review of algorithms for filterin..."
...They were reported to provide state-of-the-art performance [14]....
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...Sparsity-based approaches are reported to achieve the state-of-the-art performance [14], though at a high level of noise, the estimation of normal or local plane can be so poor that it leads to over-smoothing or over-sharpening [7]....
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TL;DR: This study presents a framework of a cyber–physical system to integrate these technologies and improve the overall capabilities of construction organization and management and introduces a case study of the Xiong’an citizen service center.
Abstract: The Fourth Industrial Revolution (Industry 40) is reshaping the construction industry and bringing it into an intelligent construction era Emerging technologies, such as the Building Information Modelling, Internet of Things, big data, cloud computing, and artificial intelligence, have penetrated into all stages of the building life cycle and play a significant role However, the major issue of intelligent construction is integrating multiple technologies to create more potential opportunities rather than their fragmented application Considering the various special characteristics of the construction industry and the high heterogeneity of these technologies, their integration in the construction industry is challenging and requires in-depth investigations This paper summarizes the Industry 40-related technologies involved in the construction industry based on an analysis of the characteristics of the industry Further, this study presents a framework of a cyber-physical system to integrate these technologies and improve the overall capabilities of construction organization and management A case study of the Xiong'an citizen service center is introduced to verify the technological feasibility and preliminary implementation effect of the proposed framework As forward-looking research, the significance of this paper may also to inspire more efforts in this field
95 citations
Cites background from "A review of algorithms for filterin..."
..., a laser scanner) in a 3D coordinate system, which are used to represent the external surfaces of an object [62]....
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TL;DR: A comprehensive survey of urban applications and key techniques based on MLS point clouds is conducted, including classification methods, object recognition, data registration, data fusion, and 3D city modeling.
Abstract: Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications.
75 citations
Cites background from "A review of algorithms for filterin..."
...For example, Meng et al. (2010) and Han et al. (2017) reviewed the issues of ground filtering of airborne laser scanning (ALS) data and filtering of 3D point cloud [22,23], respectively....
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TL;DR: A new intensity-based filter that differs from the existing distance- based filter, which limits the speed is proposed, which showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features.
Abstract: LiDAR sensors have the advantage of being able to generate high-resolution imaging quickly during both day and night; however, their performance is severely limited in adverse weather conditions such as snow, rain, and dense fog. Consequently, many researchers are actively working to overcome these limitations by applying sensor fusion with radar and optical cameras to LiDAR. While studies on the denoising of point clouds acquired by LiDAR in adverse weather have been conducted recently, the results are still insufficient for application to autonomous vehicles because of speed and accuracy performance limitations. Therefore, we propose a new intensity-based filter that differs from the existing distance-based filter, which limits the speed. The proposed method showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features. The intensity criteria for snow removal were derived based on an analysis of the properties of laser light and snow particles.
49 citations
Cites methods from "A review of algorithms for filterin..."
...Conventional noise filtering methods involve radius outlier removal (ROR), statistical outlier removal (SOR), and voxel grid (VG) filters [10]–[12]....
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References
More filters
04 Jan 1998
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Abstract: Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.
8,738 citations
"A review of algorithms for filterin..." refers background in this paper
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20 Jun 2005
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
Abstract: We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.
6,804 citations
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09 May 2011
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
Abstract: With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.
4,501 citations
IBM1
TL;DR: A very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology that is a linear time and space complexity algorithm and a very effective fair surface design technique.
Abstract: In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals – functions defined on polyhedral surfaces of arbitrary topology –, we reduce the problem of surface smoothing, or fairing, to low-pass filtering. We describe a very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology. As opposed to other existing optimization-based fairing methods, which are computationally more expensive, this is a linear time and space complexity algorithm. With this algorithm, fairing very large surfaces, such as those obtained from volumetric medical data, becomes affordable. By combining this algorithm with surface subdivision methods we obtain a very effective fair surface design technique. We then extend the analysis, and modify the algorithm accordingly, to accommodate different types of constraints. Some constraints can be imposed without any modification of the algorithm, while others require the solution of a small associated linear system of equations. In particular, vertex location constraints, vertex normal constraints, and surface normal discontinuities across curves embedded in the surface, can be imposed with this technique. CR
2,004 citations
"A review of algorithms for filterin..." refers methods in this paper
...Based on Taubin’s method [70] that applied the Laplacian operators to filter the mesh, Linsen [71] developed a filtering operator...
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Proceedings Article•
01 Jan 1994TL;DR: An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule.
Abstract: An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.
1,806 citations