DVONet: Unsupervised Monocular Depth Estimation and Visual Odometry This paper proposes an unsupervised learning framework for monocular depth estimation and visual odometry (VO), referred to as DVONet. The framework is trained using stereo image sequences and is able to estimate absolutescale scene depth and camera poses from monocular images. To mitigate the effect of stereo occlusions in training and improve the depth estimation, leftright occlusion mask is introduced. In addition, a novel VO network is proposed where the feature extraction network is shared between pose estimation and optical flow estimation. The proposed DVONet achieves stateoftheart results for both depth estimation and VO tasks on the KITTI driving dataset, outperforming the existing unsupervised methods and being comparable to the traditional ones.

Unsupervised Monocular Depth Estimation with Encoderdecoder Network * Although fully supervised learning methods could achieve good results for monocular depth estimation, it is very expensive to obtain ground truth depth data for training. In this paper, an encoderdecoder network is proposed to estimate monocular depth with unsupervised learning. During training, this network is able to enhance the weight of the effective features in the image by learning. Therefore, the encoder is able to extract more effective features and the decoder generates more targeted results. Compared to supervised learning, the proposed network is trained by stereo pairs and image reconstruction error, rather than ground truth depth. The proposed network achieves competitive results for monocular depth estimation on the KITTI driving dataset.