为了提高多视图深度估计结果精度,提出一种基于自适应空间特征增强的多视图深度估计算法.设计了由改进后的特征金字塔网络(feature pyramid network,FPN)和自适应空间特征增强(adaptive space feature enhancement,ASFE)组成的多尺度特征提取模块,获取到具有全局上下文信息和位置信息的多尺度特征图像.通过残差学习网络对深度图进行优化,防止多次卷积操作出现重建边缘模糊的问题.通过分类的思想构建focal loss函数增强网络模型的判断能力.由实验结果可知,该算法在DTU(technical university of denmark)数据集上和CasMVSNet(Cascade MVSNet)算法相比,在整体精度误差、运行时间、显存资源占用上分别降低了14.08%、72.15%、4.62%.在Tanks and Temples数据集整体评价指标Mean上该模型优于其他算法,证明提出的基于自适应空间特征增强的多视图深度估计算法的有效性.
Multi-view Depth Estimation Based on Adaptive Space Feature Enhancement
A multi-view depth estimation algorithm based on adaptive space feature enhancement(ASFE)is presented to improve the multi-view depth estimation accuracy.A multi-scale feature extraction module composed of an improved feature pyramid network(FPN)and ASFE is designed.This module obtains multi-scale feature maps with global context-aware information and coordinate information.The residual learning network is used to optimize the depth map to prevent the problem of blurred reconstructed edges in multiple convolution operations.The proposed algorithm constructs a focal loss function through the idea of classification to enhance the prediction ability of the network model.The experimental results show that on the technical university of denmark(DTU)dataset,compared with the cascade MVSNet(CasMVSNet)method,the proposed method reduces overall accuracy error,running time,and video memory resource occupation by 14.08%,72.15%,and 4.62%,respectively.The Mean of the model on the Tanks and Temples dataset is superior to other algorithms,which proves the effectiveness of the proposed multi-view depth estimation algorithm based on ASFE.
multi-view depth estimationadaptive space feature enhancementresidual learning networkconvolution operationfocal loss function
NETL
NSTL
万方数据
维普
