PDE-Based Graph Signal Processing for 3-D Color Point Clouds: Opportunities for cultural heritage

Lozes, François; Elmoataz, Abderrahim; Lézoray, Olivier

doi:10.1109/msp.2015.2408631

Cited by 44 publications

(21 citation statements)

References 13 publications

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“…This problem can be solved efficiently by the higher order orthogonal iterations (HOOI) [22] method. In this paper, we use the HOOI algorithm to solve problem (2), and understand the core tensor as the frequency domain similar to the PCA case. Then by the hard thresholding on the core tensor, we remove the noise of the point cloud.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In recent years, the low-cost and high-resolution scanners of point cloud are becoming available, and have been promoting the wide applications of point cloud processing in various areas, e.g., remote sensing [1], cultural heritage [2] and geographic information system [3]. However, because of the physical constraints, the raw point cloud data is always corrupted with noises, which has made the denoising an important step for further processing in point cloud.…”

Section: Introductionmentioning

confidence: 99%

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Point Cloud Denoising Based on Tensor Tucker Decomposition

Zhang

Tran

2019

2019 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose a new algorithm for point cloud denoising based on the tensor Tucker decomposition. We first represent the local surface patches of a noisy point cloud to be matrices by their distances to a reference point, and stack the similar patch matrices to be a 3rd order tensor. Then we use the Tucker decomposition to compress this patch tensor to be a core tensor of smaller size. We consider this core tensor as the frequency domain and remove the noise by manipulating the hard thresholding. Finally, all the fibers of the denoised patch tensor are placed back, and the average is taken if there are more than one estimators overlapped. The experimental evaluation shows that the proposed algorithm outperforms the state-of-the-art graph Laplacian regularized (GLR) algorithm when the Gaussian noise is high (σ = 0.1), and the GLR algorithm is better in lower noise cases (σ = 0.04, 0.05, 0.08).

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point Cloud Denoising Based on Tensor Tucker Decomposition

Zhang

Tran

2019

2019 IEEE International Conference on Image Processing (ICIP)

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“…Rather than binning point clouds into voxels, graph-based approaches fit a graph with one vertex for each point and edges between nearby points, and then operate on the graph. The effectiveness of GSP for processing 3D point cloud data has been demonstrated in applications such as data visualization, in-painting, and compression [10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

A Graph-CNN for 3D Point Cloud Classification

Zhang

Rabbat

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

156

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Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same number of neighbors). Thus, Graph-CNNs have huge potential to deal with 3D point cloud data which has been obtained from sampling a manifold. In this paper we develop a Graph-CNN for classifying 3D point cloud data, called PointGCN 1 . The architecture combines localized graph convolutions with two types of graph downsampling operations (also known as pooling). By the effective exploration of the point cloud local structure using the Graph-CNN, the proposed architecture achieves competitive performance on the 3D object classification benchmark ModelNet, and our architecture is more stable than competing schemes.

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“…However, this method produces unsatisfactory results on large holes, especially if the underlying surface is complex. In [10], a plane tangent to the missing part is determined and the hole boundary is projected onto this plane. Then, its convex hull is computed and points are created in such a way that the sampling allows to cover a dilated version of the convex hull.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Nonrigid Inpainting of Three-Dimensional Point Cloud Geometry

Dinesh¹,

Bajić²,

Cheung³

2018

IEEE Signal Process. Lett.

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In this letter, we introduce several algorithms for geometry inpainting of 3D point clouds with large holes. The algorithms are examplar-based: hole filling is performed iteratively using templates near the hole boundary to find the best matching regions elsewhere in the cloud, from where existing points are transferred to the hole. We propose two improvements over the previous work on exemplar-based hole filling. The first one is adaptive template size selection in each iteration, which simultaneously leads to higher accuracy and lower execution time. The second improvement is a non-rigid transformation to better align the candidate set of points with the template before the point transfer, which leads to even higher accuracy. We demonstrate the algorithms' ability to fill holes that are difficult or impossible to fill by existing methods.

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PDE-Based Graph Signal Processing for 3-D Color Point Clouds: Opportunities for cultural heritage

Cited by 44 publications

References 13 publications

Point Cloud Denoising Based on Tensor Tucker Decomposition

Point Cloud Denoising Based on Tensor Tucker Decomposition

A Graph-CNN for 3D Point Cloud Classification

Adaptive Nonrigid Inpainting of Three-Dimensional Point Cloud Geometry

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