关键词: 单目图像深度估计, 卷积神经网络, 深度残差网络, 稀疏深度测量

Abstract:

Aiming at the problems of low accuracy and slow speed in the depth estimation of monocular images by traditional methods, a fully convolutional encoding-decoding network model is proposed, which takes the sparse depth sample set and RGB image as input. The encoder layer is composed of Resnet and a convolution layer. The decoder layer is composed of four up-sampling layers and a bilinear up-sampling layer. The up-sampling layer uses the up-convolution module and the up-projection module to cross use, which effectively reduces the chessboard effect and retains the predicted depth. At the same time, full convolution is used in the model to reduce the parameters and improve the prediction speed. The validity and superiority of the network model are verified on the NYU-Depth-v2 dataset. The experimental results show that compared with the multi-scale convolution neural network, the accuracy of the model is improved by about 4% on [δ<1.25], and the RMSE error index is reduced by about 11%； compared with the RGB image alone, the RMSE error is reduced by about 26% when 100 spatial random depth samples are added.

Key words: monocular image depth estimation, convolution neural network, depth residual network, sparse depth measurement