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2018影响因子

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## 留言板

 引用本文: 李雷, 徐浩, 吴素萍. 基于DDPG的三维重建模糊概率点推理. 自动化学报, 2021, x(x): 1−14
Li Lei, Xu Hao, Wu Su-Ping. Fuzzy probability points reasoning for 3D reconstructionvia deep deterministic policy gradient. Acta Automatica Sinica, 2021, x(x): 1−14 doi: 10.16383/j.aas.c200543
 Citation: Li Lei, Xu Hao, Wu Su-Ping. Fuzzy probability points reasoning for 3D reconstructionvia deep deterministic policy gradient. Acta Automatica Sinica, 2021, x(x): 1−14

## Fuzzy Probability Points Reasoning for 3D Reconstructionvia Deep Deterministic Policy Gradient

Funds: Supported by National Natural Science Foundation of China (62062056, 61662059)
###### Author Bio: LI Lei　Master student of the School of Information Engine-ering, Ningxia University. His research interest covers 3D object reconstruction, face reconstruction and landmark alignment, image processing, computer vision and pattern recognition XU Hao　Master student of the School of Information En- gineering,Ningxia University. His research interest covers computer vision and 3D human pose estimation WU Su-Ping　Professor of the School of Information Engine- erring, Ningxia University. Her research interest covers 3D reconstruction, computer vision, pattern recognition, parallel distributed processing and big data. Corresponding author of this paper
• 摘要: 单视图物体三维重建是一个长期存在的具有挑战性的问题. 为了解决具有复杂拓扑结构的物体以及一些高保真度的表面细节信息仍然难以准确进行恢复的问题, 本文提出了一种基于深度强化学习的算法深度确定性策略梯度(Deep deterministic policy gradient, DDPG)对三维重建中模糊概率点进行再推理, 实现了具有高保真和丰富细节的单视图三维重建. 本文的方法是端到端的, 包括以下四个部分: 拟合物体三维形状的动态分支代偿网络的学习过程, 聚合模糊概率点周围点的邻域路由机制, 注意力机制引导的信息聚合和基于深度强化学习算法的模糊概率调整. 本文在公开的大规模三维形状数据集上进行了大量的实验证明了本文方法的正确性和有效性. 本文提出的方法结合了强化学习和深度学习, 聚合了模糊概率点周围的局部信息和图像全局信息, 从而有效的提升了模型对复杂拓扑结构和高保真度的细节信息的重建能力.
• 图  1  基于深度学习的单视图三维重建中三种表示形状

Fig.  1  Three representation shapes for single-view 3D reconstruction based on deep learning

图  2  (a)为输入图像 (b)DISN结果 (c)本文方法的结果

Fig.  2  Single image reconstruction using a DISN, and our method on real images

图  3  MNGD框架的整体流程图

Fig.  3  The workflow of the proposed MNGD framework

图  4  动态分支代偿网络框架图

Fig.  4  The framework of the dynamic branch compensation network

图  5  邻域路由过程

Fig.  5  The whole process of neighbor routing

图  6  聚合特征时的注意力机制

Fig.  6  Attention mechanism when features are aggregated

图  7  卷积可视化与网格生成过程

Fig.  7  Convolution visualization and mesh generation process

图  8  ShapeNet数据集上的定性结果

Fig.  8  Qualitative results on the ShapeNet dataset

图  9  Online Products dataset的定性结果

Fig.  9  Qualitative results on Online Products dataset

图  10  消融实验的定性结果

Fig.  10  Qualitative results of Ablation Study

图  11  MNGD随机调整100张图片中模糊概率点的结果

Fig.  11  The result of MNGD adjusting the fuzzy probability points in 100 random images

图  12  ShapeNet上所有类别的定性结果

Fig.  12  Qualitative results on ShapeNet of all categories

图  13  单视图三维重建中具有挑战性案例

Fig.  13  Challenging cases in single-view 3D reconstruction

##### 计量
• 文章访问数:  244
• HTML全文浏览量:  38
• 被引次数: 0
##### 出版历程
• 收稿日期:  2020-07-13
• 修回日期:  2020-12-05
• 网络出版日期:  2021-03-02

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