A review of algorithms for filtering the 3D point cloud

Han, Xuefei; Jin, Jesse S.; Wang, Mingjie; Jiang, Wei; Gao, Lei; Xiao, Liping

doi:10.1016/j.image.2017.05.009

Cited by 270 publications

(138 citation statements)

References 63 publications

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“…Point cloud denoising and outlier removal have a long and rich history in diverse areas of computer science and a full overview is beyond the scope of the current article. Below, we briefly review the main general trends for addressing these problems, while concentrating on solutions most closely related to ours, and refer the interested reader to a recent survey [HJW*17].…”

Section: Related Workmentioning

confidence: 99%

“…Denoising is then achieved by projecting the points onto the estimated local surfaces. These techniques are very robust for small noise but can lead to significant over‐smoothing or over‐sharpening for high noise levels [MC17, HJW*17].…”

Section: Related Workmentioning

confidence: 99%

“…Decades of research have produced many variants of denoising approaches targeted for different surface types and noise models (see survey [HJW*17]). Such approaches can be broadly categorized as: classifying points as outliers using statistical methods, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Rakotosaona

Barbera

Guerrero

et al. 2019

Computer Graphics Forum

206

196

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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).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Rakotosaona

Barbera

Guerrero

et al. 2019

Computer Graphics Forum

206

196

View full text Add to dashboard Cite

show abstract

“…On the other hand, noise produced in the depth capturing process leads to depth estimation errors, which also affects the construction of spatial connectivity in point clouds. Point cloud filtering methods (see [14] for details) are usually applied to reduce noise in the point cloud.…”

Section: A Spatial Connectivity In Point Cloudsmentioning

confidence: 99%

Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes

Lin

Casas

Pardàs

2018

IEEE Trans. on Image Process.

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Abstract-Video segmentation is an important building block for high level applications such as scene understanding and interaction analysis. While outstanding results are achieved in this field by state-of-the-art learning and model based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provides actual world 3D geometry compared to 2D images. The explicit geometry in RGBD data greatly helps in computer vision tasks, but the lack of annotations in this type of data may also hinder the extension of learning based methods to RGBD.In this paper, we present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry in RGBD. Our proposal is only based on low level features, such as connectivity and compactness. We exploit temporal coherence by representing the rough estimation of objects in a single frame with a hierarchical structure, and propagating this hierarchy along time. The hierarchical structure provides an efficient way to establish temporal correspondences at different scales of object-connectivity, and to temporally manage the splits and merges of objects. This allows updating the segmentation according to the evidence observed in the history. The proposed method is evaluated on several challenging datasets, with promising results for the presented approach.

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“…If the number of points in the seeds is smaller than minPts, then the current points can be regarded as noise and it should be denoted as noise point (Algorithm 2, lines [13][14]. Otherwise, a valid label is assigned to all points in the seeds and the current points (Algorithm 2, lines [15][16][17][18][19][20]. For every points in seeds, their k neighboring positions are also searched (Algorithm 2, line 21); if the number of neighboring points within ep from a given point in seeds is greater than minPts, those points will also be labeled with the same label (Algorithm 2, lines 26-37).…”

Section: Non-ground Object Clustering Processmentioning

confidence: 99%

Range image-based density-based spatial clustering of application with noise clustering method of three-dimensional point clouds

Wen

Cho

Chae

et al. 2018

International Journal of Advanced Robotic Systems

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Clustering plays an important role in processing light detection and ranging points in the autonomous perception tasks of robots. Clustering usually occurs near the start of processing three-dimensional point clouds obtained from light detection and ranging for detection and classification. Therefore, errors caused by clustering will directly affect the detection and classification accuracy. In this article, a clustering method is presented that combines density-based spatial clustering of application with noise and two-dimensional range image composed by scan lines of light detection and ranging based on the order of generation time. The results show that the proposed method achieves state-of-the-art performance in aspect of time efficiency and clustering accuracy. A ground extraction method based on scan line is also presented in this article, which has strong ability to separate ground points and non-ground points.

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A review of algorithms for filtering the 3D point cloud

Cited by 270 publications

References 63 publications

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes

Range image-based density-based spatial clustering of application with noise clustering method of three-dimensional point clouds

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