Point Cloud Denoising Using Robust Principal Component Analysis

Narváez, Esmeide A Leal; Leal, Nallig

doi:10.5220/0001358900510058

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…Normal smoothing methods are focused on estimating noise-free normals, followed by an updating of the position of the points based on the clean normals. In [21], the authors used a robust version of principal component analysis (PCA) to estimate the normal vector; the authors proposed weight factors that were inversely proportional to the sum of the distance to the mean. The weights defined in this way make the method robust to outliers and noise.…”

Section: Normal Smoothing-based Methodsmentioning

confidence: 99%

Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Leal

Sánchez-Torres

Branch

2020

Sensors

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Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.

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Section: Normal Smoothing-based Methodsmentioning

confidence: 99%

Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Leal

Sánchez-Torres

Branch

2020

Sensors

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“…Błędy systematyczne w pomiarach kamerami ToF są konsekwencją: − kwantyzacji pomiarów [10], − zmian temperatury urządzenia pomiarowego [2], − kąta padania wiązki lasera na przeszkodę [11], − koloru i właściwości powierzchni przeszkody (np. refleksyjności) [2, 11, 37], − warunków oświetleniowych [11], − szumu generowanego przez urządzenie dokonujące akwizycji [18].…”

Section: Błędy W Pomiarach Kamerami Time-of-flightunclassified

Point Cloud Filtering Using 2D-3D Matching Method

Rzepka¹,

Kulczykowski²,

Wittels³

2022

PAR

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Precision is a key feature for the development of 3D measurement systems. Time-of-flight cameras used for such measurements create point clouds containing a lot of noise, which may not be useful for further analysis. In our research to solve this problem, we propose a new method for precise point cloud filtering. We use 2D information from a telecentric lens camera to remove outlier points from 3D measurements recorded with a Time-of-Flight camera. The use of a telecentric camera allows us to obtain the most precise information about the contour of an object, which allows us to accurately filter the object reconstruction in 3D.

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PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

Mao

Wen

et al. 2022

Lecture Notes in Computer Science

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Point Cloud Denoising Using Robust Principal Component Analysis

Cited by 8 publications

References 9 publications

Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Point Cloud Filtering Using 2D-3D Matching Method

PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

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