A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View

Cai-xia, Liu; Kong, Dehui; Wang, Shaofan; Li, Jinghua; Yin, Baocai

doi:10.1145/3506733

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…This technology is widely used in sectors such as industrial design [15,26], medical research [10,28], shape recognition [20], spatiotemporal analysis [5,13], and digital protection of cultural relics [27,31]. High-quality feature extraction can provide strong support for subsequent point cloud registration, splicing, and surface reconstruction [18,35]. At present, much intensive research has been conducted on the feature extraction of 3D models, which can be mainly divided into feature extraction based on the mesh model and feature extraction based on the point cloud.…”

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

Jia

Pang

Xie

2022

Circuits Syst Signal Process

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The feature extraction of a point cloud fragment model is the basis of fragment splicing, which provides the technical support for research on the segmentation, splicing, and restoration of fragment surfaces. High-quality feature extraction, however, is a complicated process due to the diversity of the surface information of a fragment model. For this subject, a high-efficient point cloud feature extraction method was proposed to address a new method for extracting feature lines. First, the projection distance feature of the point cloud model was calculated to identify the potential feature points. Furthermore, the local information of the possible feature points was used to construct the adaptive neighborhoods for identifying the feature points based on neighborhoods of the model. The clustering fusion of the feature points was proposed according to the discrimination threshold values of the feature points. Finally, the Laplace operator was utilized to refine and connect the feature points to form smooth feature lines. The experimental results showed that the proposed method was automatic, highly efficient, and with good adaptability that could effectively extract the detailed features and construct the complete feature lines. Moreover, results showed that the provided framework could extract the features of simple structure models and be feasible to a certain extent for fragment models with abundant features.

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Section: Introductionmentioning

confidence: 99%

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

Jia

Pang

Xie

2022

Circuits Syst Signal Process

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UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction

Zhu,

Yang,

et al. 2023

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

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Progressive Transformer Machine for Natural Character Reenactment

Yang

Chen

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Character reenactment aims to control a target person’s full head movement by a driving monocular sequence which is made up of the driving character video. Current algorithms utilize convolution neural networks in generative adversarial networks, which extract historical and geometric information to iteratively generate video frames. However, convolution neural networks can merely capture local information with limited receptive fields and ignore global dependencies that play a crucial role in face synthesis, leading to generating unnatural video frames. In this work, we design a progressive transformer module that introduces multi-head self-attention with convolution refinement to simultaneously capture global-local dependencies. Specifically, we utilize the non-lapping window-based multi-head self-attention mechanism with hierarchical architecture to obtain the larger receptive fields at low-resolution feature map and thus extract global information. To better model local dependencies, we introduce the convolution operation to further refine the attentional weight in the multi-head self-attention mechanism. Finally, we use several stacked progressive transformer modules with the down-sampling operation to encode information of appearance information of previously-generated frames and parameterized 3D face information of the current frame. Similarly, we use several stacked progressive transformer modules with the up-sampling operation to iteratively generate video frames. In this way, it can capture glocal-local information to facilitate generating video frames that are globally natural while preserving sharp outlines and rich detail information. Extensive experiments on several standard benchmarks suggest that the proposed method outperforms current leading algorithms.

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A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View

Cited by 5 publications

References 26 publications

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction

Progressive Transformer Machine for Natural Character Reenactment

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