In this paper, we propose a self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos. Although self-supervised learning has achieved impressive performance in depth estimation using pose estimation as an auxiliary task, it still shows undesired results for the pose estimation. Different from streetscape datasets, the laparoscope motion is limited by the minimally invasive surgery settings. It is challenging to estimate laparoscopes’ poses with complex rotations from RGB images. To address this issue, we propose an improved self-supervised depth estimation method with relative pose loss for laparoscopic videos. Furthermore, we adopt homoscedastic uncertainty to weigh our loss function to balance each subtask. In addition to the evaluation for known datasets, we also tested the generalization ability of our proposed method using known datasets and unseen datasets. The experimental results showed that our proposed method outperforms baseline for depth estimation and pose estimation on known datasets and had competitive results on unseen datasets. For depth estimation, the proposed method had about 10.5% improvement on RMSE evaluation compared to the baseline.
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 are 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.
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