Template-Based Reconstruction of Surface Mesh Animation from Point Cloud Animation

Park, Sang-Il; Lim, Seong-Jae

doi:10.4218/etrij.14.0113.1181

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…To reduce the influence of redundant data such as ear, neck, hair, and so on, it is necessary to cut out the facial area based on the tip of the nose which serves as the center of the ball. Because of the defects of the original data, such as point cloud noise, lack of sampling, cusp and hole, deformation, and so on [14], judging the quality of the 3D face point cloud plays an important role further.…”

Section: D Point Clouds Cut Facial Areas and Calculate Curvatementioning

confidence: 99%

Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

2022

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3D face point cloud quality judgment is an important task in 3D face recognition and 3D construction, as an auxiliary task of face recognition to predict the quality of a given image. However, projection-based quality judgment methods always ignore the value of multi-view 2D image features and 3D point cloud features. We propose a Feature Fusion Network (FFN) to address the above problems. We first preprocess the 3D point cloud obtained by the CCD camera, cut out the face area, and then use the point cloud data for multi-angle rotation and the corresponding 2D plane depth map projection as input. Secondly, Dynamic Graph Convolutional Neural Network (DGCNN) was trained for point cloud learning and ShuffleNet was trained for image learning. Then, the middle layer features of the two network modules were extracted and concat to fine-tune the whole network. Finally, three fully connected layers were used to realize the five-class classification of the 3D face point cloud (excellent, ordinary, stripe, burr, deformation). The proposed FFN achieved the classification accuracy of 83.7%, which was 5.8% higher than that of ShuffleNet and 2.2% higher than that of DGCNN. The experimental results show that concat depth map features and point cloud features can achieve the complementary effect between different features.INDEX TERMS Face point cloud, point cloud feature, depth map, concat features, quality judgment.

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Section: D Point Clouds Cut Facial Areas and Calculate Curvatementioning

confidence: 99%

Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

2022

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“…The same is true for [28,29,30] and unsuitable in our setting, due to extremely low (2-8) number of correspondences available per part, that additionally may not even be well defined for noisy and incomplete real-world scans. Many methods [29,31,32,33] focus on automatic posing of human models, dense surface tracking and similar applications. However, while producing compelling results, the methods implicitly use the assumption that a template mesh and its target shape are very similar; as a result, the semantic parts of individual 3D objects are not changed either in shape or relative scale.…”

Section: Related Workmentioning

confidence: 99%

CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

Ishimtsev¹,

Bokhovkin²,

Artemov³

et al. 2020

Preprint

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Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios. Unfortunately, CAD model retrieval is limited by the availability of models in standard 3D shape collections (e.g., ShapeNet). In this work, we address this shortcoming by introducing CAD-Deform 1 , a method which obtains more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models. Our key contribution is a new non-rigid deformation model incorporating smooth transformations and preservation of sharp features, that simultaneously achieves very tight fits from CAD models to the 3D scan and maintains the clean, high-quality surface properties of hand-modeled CAD objects. A series of thorough experiments demonstrate that our method achieves significantly tighter scan-to-CAD fits, allowing a more accurate digital replica of the scanned real-world environment while preserving important geometric features present in synthetic CAD environments.

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“…3D human body models are widely used in various fields of computer vision, such as clothing design [1], [2], [3], [4], animation production [5], [6], [7], and contactless body measurement [8], [9], etc. Typically, different from other 3D objects, the orientation of a body is assumed to be not only upright but also face to the specified direction in a default coordinate system.…”

Section: Introductionmentioning

confidence: 99%

OrienNormNet: Orientation Normalization of 3D Body Models

ZHAO¹,

DAI²,

HU³

et al. 2022

Proceedings of 3DBODY.TECH 2022 - 13th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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3D human body models are widely used in human-centric industrial applications, including healthcare, fashion design, body biometrics extraction, and computer animation. Prior to processing and analyzing body models, it is significantly important to rotate them to the same orientation. For instance, body measurement systems and virtual try-on systems usually assume the orientation of the body is known. These systems will output incorrect results or report errors without the correct orientation information. Unfortunately, the orientations of scanned bodies are different in practice since they are in different coordinate systems due to the setup variations of scanners. To automatically normalize the orientations of bodies is a challenging task due to the presence of pose variations, noises, and holes during 3D body scanning. In this study, we propose a novel deep learning-based method dubbed OrienNormNet for normalizing the orientation of 3D body models. As shown in Figure 1 and Figure 2, OrienNormNet directly consumes raw point clouds or mesh vertices, and it is applied in an iterative manner. First, the centroids of point clouds are translated to the origin to obtain zero-centered point clouds. Next, OrienNormNet consumes zero-centered raw point clouds and outputs coarse axis rotation angles. Finally, OrienNormNet takes the coarsely rotated point clouds from the previous processing as an updated input and outputs the refined axis rotation angles. By applying the obtained coarse and fine axis rotation angles, thousands of bodies can be adjusted to the same orientation in a few seconds. Experimental results based on synthetic datasets as well as real-world datasets validated the effectiveness of our idea.

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Template-Based Reconstruction of Surface Mesh Animation from Point Cloud Animation

Cited by 7 publications

References 18 publications

Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

OrienNormNet: Orientation Normalization of 3D Body Models

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