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算法深度确定性策略梯度(Deep deterministic policy gradient,DDPG)的方法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,从而有效地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.
3D object reconstruction from a single-view image is a long-standing challenging problem.In order to address the difficulty of accurately predicting the objects of complex topologies and some high-fidelity surface details,we propose a new method based on DDPG(Deep deterministic policy gradient)to reason the fuzzy probability points in 3D reconstruction and achieve high-quality detail-rich reconstruction result of single-view image.Our method is end-to-end and includes four parts:the dynamic branch compensation network learning process to fit the 3D shape of objects,the neighborhood routing mechanism to aggregate the points around the fuzzy probability points,the attention guidance mechanism to aggregate the information,and the deep reinforcement learning algorithm to perform probabilistic reasoning.Extensive experiments on a large-scale public 3D shape dataset demonstrate the validity and efficiency of our method.Our method combines reinforcement learning and deep learning,aggregates local information around the fuzzy probability points and global information of the image,and effectively improves the model's ability to reconstruct complex topologies and high-fidelity details.
作者
李雷
徐浩
吴素萍
LI Lei;XU Hao;WU Su-Ping(School of Information Engineering,Ningxia University,Yinchuan 750021)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第4期1105-1118,共14页
Acta Automatica Sinica
基金
国家自然科学基金(62062056,61662059)资助。
关键词
三维重建
强化学习
深度学习
注意力机制
信息聚合
3D reconstruction
reinforcement learning
deep learning
attention mechanism
information aggregation
