基于轻量化深度卷积循环网络的MVS方法OA北大核心CSTPCD

Based on deep learning MVS methods,neural networks suffered from a large number of parameters and high GPU memory consumption.To address this issue,a lightweight deep convolutional recurrent network recurrent network-based MVS method was proposed.Firstly,the original images passed through a lightweight multi-scale fea-ture extraction network to obtain high-level semantic feature maps.Then,a sparse cost volume to reduce the com-putational workload was constructed.Next,GPU memory consumption was reduced by using a simple plane sweep-ing technique that utilized by a convolutional recurrent network for cost volume regularization.Finally,sparse depth maps were extended to dense depth maps using an extension module.With a refinement algorithm,the proposed approach achieved a certain level of accuracy.The proposed approach was compared to state-of-the-art methods on the DTU dataset including traditonal MVS methods Camp,Furu,Tola,and Gipuma,and also including deep learn-ing-based MVS methods SurfaceNet,PU-Net,MVSNet,R-MVSNet,Point-MVSNet,Fast-MVSNet,GBI-Net,and TransMVSNet.The results demonstrated that the proposed approach reduced GPU consumption to approximately 3.1 GB during the prediction stage,and the differences in precision compared to other methods were relatively small.