No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models

Zhang, Zicheng; Sun, Wei; Min, Xiongkuo; Wang, Tao; Lu, Wei; Zhai, Guangtao

doi:10.48550/arxiv.2107.02041

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…In this work, we estimate the visual quality based on the effectiveness of different types of features, including both color and geometry, as proposed in [58]. This study aims to quantitatively estimate the quality of the generated 3D face morphing point clouds and the bona fide 3D face point clouds to quantify the quality of the proposed morphing generation.…”

Section: Automatic 3d Face Point Cloud Quality Estimationmentioning

confidence: 99%

3-D Face Morphing Attacks: Generation, Vulnerability and Detection

Singh,

Ramachandra

2024

IEEE Trans. Biom. Behav. Identity Sci.

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Face Recognition systems (FRS) have been found vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction toward generating face-morphing attacks in 3D. To this extent, we have introduced a novel approach based on blending the 3D face point clouds corresponding to the contributory data subjects. The proposed method will generate the 3D face morphing by projecting the input 3D face point clouds to depth maps and 2D color images, followed by the image blending and wrapping operations performed independently on the color images and depth maps. We then back-project the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes due to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments are carried out on the newly generated 3D face dataset comprised of 675 3D scans corresponding to 41 unique data subjects and the publicly available Facescape database with 100 unique identities. Experiments are performed to benchmark the vulnerability of the proposed 3D morph generation scheme against automatic 2D, 3D FRS and human observer analysis. We also present the quantitative assessment of the quality of the generated 3D face morphing models using eight different quality metrics. Finally, we have proposed three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of the 3D face morphing attack detection techniques.

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Section: Automatic 3d Face Point Cloud Quality Estimationmentioning

confidence: 99%

3-D Face Morphing Attacks: Generation, Vulnerability and Detection

Singh,

Ramachandra

2024

IEEE Trans. Biom. Behav. Identity Sci.

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“…In this work, we estimate the visual quality based on the effectiveness of different types of features, including both color and geometry, as proposed in [45]. This study aims to Figure 7 shows the box plot of the eight different quality metrics for both 3D bona fide and 3D morphing point clouds.…”

Section: Automatic 3d Face Point Cloud Quality Estimationmentioning

confidence: 99%

3D Face Morphing Attacks: Generation, Vulnerability and Detection

Singh¹,

Ramachandra²

2022

Preprint

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Face Recognition systems (FRS) have been found vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction towards generating face morphing attacks in 3D. To this extent, we have introduced a novel approach based on blending the 3D face point clouds corresponding to the contributory data subjects. The proposed method will generate the 3D face morphing by projecting the input 3D face point clouds to depth-maps & 2D color images followed by the image blending and wrapping operations performed independently on the color images and depth maps. We then back-project the 2D morphing color-map and the depthmap to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in the holes due to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments are carried out on the newly generated 3D face dataset comprised of 675 3D scans corresponding to 41 unique data subjects. Experiments are performed to benchmark the vulnerability of automatic 2D and 3D FRS and human observer analysis. We also present the quantitative assessment of the quality of the generated 3D face morphing models using eight different quality metrics. Finally, we have proposed three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of the 3D MAD algorithms.

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“…In this paper, we propose the use of geometric descriptors based on Principal Component Analysis (PCA) to estimate structural distortions in point cloud contents. Such descriptors have been used with lidar data for urban classification [14], semantic interpretation [15], semantic segmentation [16], and contour detection [17], while more recently, a subset with well-behaving distributions was employed for no-reference objective quality assessment [18]. In order to better capture local variations in the distribution of the descriptors, we adopt statistical features that can estimate average trends and dispersion in a neighborhood.…”

Section: Introductionmentioning

confidence: 99%

PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors

Alexiou¹,

Viola²,

César³

2021

Preprint

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With the increasing popularity of extended reality technology and the adoption of depth-enhanced visual data in information exchange and telecommunication systems, point clouds have emerged as a promising 3D imaging modality. Similarly to other types of content representations, visual quality predictors for point cloud data are vital for a wide range of applications, enabling perceptually optimized solutions from acquisition to rendering. Recent standardization activities on point cloud compression have urged the need for objective quality evaluation methods, driving the research community to the development of relevant algorithms. In this work, we complement existing approaches by proposing a new quality metric that compares local shape and appearance measurements between a reference and a distorted point cloud. To this aim, a large set of geometric and textural descriptors is defined, and the prediction accuracy of corresponding statistical features is evaluated in the context of quality assessment. Different combination strategies are examined, providing insights regarding the effectiveness of different metric designs. The performance of the proposed method is validated against subjectively-annotated datasets, showing better performance against state-of-the-art solutions in the majority of cases. A software implementation of the metric is made available here: https://github.com/cwi-dis/pointpca.

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No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models

Abstract: To improve the viewer's Quality of Experience (QoE) and optimize computer graphics applications, 3D model quality assessment (3D-QA) has become an important task in the multimedia area. Point cloud and mesh are the two most

Cited by 4 publications

References 31 publications

3-D Face Morphing Attacks: Generation, Vulnerability and Detection

3-D Face Morphing Attacks: Generation, Vulnerability and Detection

3D Face Morphing Attacks: Generation, Vulnerability and Detection

PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors

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