Point Cloud Denoising via Moving RPCA

Mattei, Enrico; Castrodad, Alexey

doi:10.1111/cgf.13068

Cited by 123 publications

(101 citation statements)

References 39 publications

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…Similarly, other local fitting approaches have also been used for point cloud denoising, using robust jet‐fitting with re‐projection [CP05, CP07] or various forms of bilateral filtering on point clouds [HWG*13, DDF17], which take into account both point coordinates and normal directions for better preservation of edge features. A closely related set of techniques is based on sparse representation of the point normals for better feature preservation [ASGCO10, SSW15, MC17]. Denoising is then achieved by projecting the points onto the estimated local surfaces.…”

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

249

196

View full text Add to dashboard Cite

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).

show abstract

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%

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

Rakotosaona

Barbera

Guerrero

et al. 2019

Computer Graphics Forum

249

196

View full text Add to dashboard Cite

show abstract

“…It should be noted that finding α is an off-line process and then we can determine γ opt for a given noisy point cloud based on its estimated noise variance. Results for Noise Variance Estimation: The proposed noise variance estimation method is compared with the PCA-based method mentioned in Section VI-D. Point cloud models we use are Bunny provided in [43], Gargoyle, DC, and Daratech provided in [23], [27]. To show the performance of noise variance estimation quantitatively, the relative percentage error ( ) in terms of the noise standard deviation (SD) is computed as follows:…”

Section: Resultsmentioning

confidence: 99%

“…However, σ 2 is unknown in general. In the point cloud denoising literature [19], [23], [31], [32], [52], typically σ 2 is assumed known or γ opt is tuned experimentally for best performanceneither is realizable in practice when only a noisy point cloud is given as input.…”

Section: A Weight Parameter and Noise Variancementioning

confidence: 99%

Local 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation

Dinesh

Cheung

Bajić

et al. 2018

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)

View full text Add to dashboard Cite

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are often corrupted with noise. Building on recent advances in graph signal processing, we design local algorithms for 3D point cloud denoising. Specifically, we design a reweighted graph Laplacian regularizer (RGLR) for surface normals and demonstrate its merits in rotation invariance, promotion of piecewise smoothness, and ease of optimization. Using RGLR as a signal prior, we formulate an optimization problem with a general p-norm fidelity term that can explicitly model two types of independent noise: small but non-sparse noise (using 2 fidelity term) and large but sparser noise (using 1 fidelity term).To establish a linear relationship between normals and 3D point coordinates, we first perform bipartite graph approximation to divide the point cloud into two disjoint node sets (red and blue). We then optimize the red and blue nodes' coordinates alternately. For 2-norm fidelity term, we iteratively solve an unconstrained quadratic programming (QP) problem, efficiently computed using conjugate gradient with a bounded condition number to ensure numerical stability. For 1-norm fidelity term, we iteratively minimize an 1-2 cost function sing accelerated proximal gradient (APG), where a good step size is chosen via Lipschitz continuity analysis. Finally, we propose simple mean and median filters for flat patches of a given point cloud to estimate the noise variance given the noise type, which in turn is used to compute a weight parameter trading off the fidelity term and signal prior in the problem formulation. Extensive experiments show state-of-the-art denoising performance among local methods using our proposed algorithms.

show abstract

“…In order to evaluate the proposed method, we compare with three competitive approaches: the mean-curve based featurepreserving simplification in [11], the graph-based contourextracted resampling in [3], and the uniform sampling method using the voxel-grid in PCL [19]. We test on several point clouds, including Daratech, Anchor, Armadillo, Shutter [20], and Hand 1 .…”

Section: Methodsmentioning

confidence: 99%

Feature Preserving and Uniformity-Controllable Point Cloud Simplification on Graph

Guo

2019

2019 IEEE International Conference on Multimedia and Expo (ICME)

View full text Add to dashboard Cite

With the development of 3D sensing technologies, point clouds have attracted increasing attention in a variety of applications for 3D object representation, such as autonomous driving, 3D immersive tele-presence and heritage reconstruction. However, it is challenging to process large-scale point clouds in terms of both computation time and storage due to the tremendous amounts of data. Hence, we propose a point cloud simplification algorithm, aiming to strike a balance between preserving sharp features and keeping uniform density during resampling. In particular, leveraging on graph spectral processing, we represent irregular point clouds naturally on graphs, and propose concise formulations of feature preservation and density uniformity based on graph filters. The problem of point cloud simplification is finally formulated as a trade-off between the two factors and efficiently solved by our proposed algorithm. Experimental results demonstrate the superiority of our method, as well as its efficient application in point cloud registration.

show abstract

Point Cloud Denoising via Moving RPCA

Cited by 123 publications

References 39 publications

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

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

Local 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation

Feature Preserving and Uniformity-Controllable Point Cloud Simplification on Graph

Contact Info

Product

Resources

About