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Akademický článek

A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications

Autor: Abubakar Sulaiman Gezawa, Yan Zhang, Qicong Wang, Lei Yunqi

Publikováno v: IEEE Access, Vol 8, Pp 57566-57593 (2020)

Deep learning approach has been used extensively in image analysis tasks. However, implementing the methods in 3D data is a bit complex because most of the previously designed deep learning architectures used 1D or 2D as input. In this work, the perf

Externí odkaz: https://doaj.org/article/e90fa98561394390a29218ffa7ffee69

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DC-GNet: Deep Mesh Relation Capturing Graph Convolution Network for 3D Human Shape Reconstruction

Autor: Zhou, Shihao, Jiang, Mengxi, Cai, Shanshan, Lei, Yunqi

In this paper, we aim to reconstruct a full 3D human shape from a single image. Previous vertex-level and parameter regression approaches reconstruct 3D human shape based on a pre-defined adjacency matrix to encode positive relations between nodes. T

Externí odkaz: http://arxiv.org/abs/2108.12384

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SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints

Autor: Wu, Weikun, Zhang, Yan, Wang, David, Lei, Yunqi

Since the PointNet was proposed, deep learning on point cloud has been the concentration of intense 3D research. However, existing point-based methods usually are not adequate to extract the local features and the spatial pattern of a point cloud for

Externí odkaz: http://arxiv.org/abs/2003.14014

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Akademický článek

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Joint Representation of Multiple Geometric Priors via a Shape Decomposition Model for Single Monocular 3D Pose Estimation

Autor: Jiang, Mengxi, Yu, Zhuliang, Li, Cuihua, Lei, Yunqi

In this paper, we aim to recover the 3D human pose from 2D body joints of a single image. The major challenge in this task is the depth ambiguity since different 3D poses may produce similar 2D poses. Although many recent advances in this problem are

Externí odkaz: http://arxiv.org/abs/1905.13466

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