Context Encoder Guided Self-Supervised Siamese Depth Estimation Based on Stereo Laparoscopic Images

摘要

This paper proposes a novel self-supervised depth estimation method guided by a context encoder. Depth estimation from stereo laparoscopic images is essential to robotic surgical navigation systems and robotic surgical platform. Recent work has shown that depth estimation of stereo image pairs can be formulated as a self-supervised learning task without ground-truth. However, most architectures based on convolutional neural lead to lose some spatial information because of the consecutive pooling and convolution operations. In order to tackle this problem, we add a contextual encoding module to the previous method. The context encoder module is formed by dense atrous convolution block and spatial pyramid pooling block that arc used to extract and merge features on different scales. Also, we add the edge-awared smoothness for predicted disparity maps. In addition, we output multi-scale disparity predictions and corresponding image reconstruction for loss calculating. In the experiments, we showed that the proposed method has about 7.79% improvement in SSIM and about 17.76% improvement in PSNR for stereo image pairs compared with previous method. Also, the disparity maps and reconstructed images given by the proposed method have significant enhancements compared with the previous method.