A robust and rotationally invariant local surface descriptor with applications to non-local mesh processing

Maximo, André; Patro, Rob; Varshney, Amitabh; Farias, Ricardo

doi:10.1016/j.gmod.2011.05.002

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…This extends recent work [13,25,24] with an adaptive patch size. First, the nodes contained in a sphere S ε ðv…”

Section: Local Adaptive Patches Constructionsupporting

confidence: 88%

“…In [12], Wu et al detect salient regions with a descriptor measuring the local height field into the neighborhood of each vertex; a square map of projection heights [13] is generated to denote its form. Local and global saliencies are computed for each vertex.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Thus, the patch at vertex v i is represented by a rectangle of size T x ðv i Þ Â T y ðv i Þ centered at v i . It should be noted that usually a patch is represented by a square of fixed size [13,24,25] and does not allow us to get an adaptative local descriptor depending on the local geometry. This local patch is then divided into l Â l cells in order to specify the indexes (in x-and y-axis) of the cell in which a vertex v j A S ϵ ðv i Þ is projected:…”

Section: Local Adaptive Patches Constructionmentioning

confidence: 99%

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Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Nouri

Charrier

Lézoray

2015

Signal Processing: Image Communication

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a b s t r a c tOur visual attention is attracted by specific areas into 3D objects (represented by meshes). This visual attention depends on the degree of saliency exposed by these areas. In this paper, we propose a novel multi-scale approach for detecting salient regions. To do so, we define a local surface descriptor based on patches of adaptive size and filled in with a local height field. The single-scale saliency of a vertex is defined as its degree measure in the mesh with edges weights computed from adaptive patch similarities weighted by the local curvature. Finally, the multi-scale saliency is defined as the average of single-scale saliencies weighted by their respective entropies. The contribution of the multi-scale aspect is analyzed and showed through the different results. The strength and the stability of our approach with respect to noise and simplification are also studied. Our approach is compared to the state-of-the-art and presents competitive results.

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“…This extends recent work [13,25,24] with an adaptive patch size. First, the nodes contained in a sphere S ε ðv…”

Section: Local Adaptive Patches Constructionsupporting

confidence: 88%

Section: State-of-the-artmentioning

confidence: 99%

Section: Local Adaptive Patches Constructionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Nouri

Charrier

Lézoray

2015

Signal Processing: Image Communication

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“…Research in the field of digital image filtering has been adapted for point cloud filtering algorithms but it is not direct due to the irregularity, shrinkage and drifting of point clouds. In recent years, a number of filtering methods for 3D point cloud have been developed, such as data clustering [12,13], density-based function [14,15], principal component analysis (PCA) [16][17][18], locally optimal projection (LOP) [19,20], MLS [21,22], nonlocal methods [23,24] and partial differential equations (PDEs) [25,26]. Zaman et al [14] proposed a point cloud denoising method based on a kernel density function.…”

Section: Introductionmentioning

confidence: 99%

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Zhou

2019

Remote Sensing

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Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

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“…Leifman et al [6] defined surface regions of interest by combining vertex distinctness (with similarities between SpinImage descriptors [7]) and mesh shape extremities. Wu et al [4] defined mesh saliency by considering both local contrast (with multi-scale similarities between local height maps [8]) and global rarity (using clustering on local contrast features). Tao et al [9] have proposed to over-segment the mesh into regions using also local height maps [8], and to use manifold ranking to define each region saliency.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale saliency of 3D colored meshes

Nouri

Charrier

Lézoray

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Mesh surface saliency detection is an important preprocessing step for many 3D applications. This paper proposes a novel saliency computation method by the use of a local vertex descriptor in the form an adaptive patch. This descriptor is used as a basis for similarity measurement and integrated into a weighted multi-scale saliency computation. Experimental results demonstrate that the proposed approach achieves competitive and innovative results, especially for 3d colored meshes.

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A robust and rotationally invariant local surface descriptor with applications to non-local mesh processing

Cited by 26 publications

References 19 publications

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

Multi-scale mesh saliency with local adaptive patches for viewpoint selection

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Multi-scale saliency of 3D colored meshes

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