Self-supervised monocular depth estimation based on image texture detail enhancement

We present a new self-supervised monocular depth estimation method with multi-scale texture detail enhancement. Based on the observation that the image texture detail and the semantic information have essential significance on the depth estimation, we propose to provide them to the network to learn more sharpness and structural integrity of depth. Firstly, we generate the filtered images and detail images by multi-scale decomposition and use a deep neural network to automatically learn their weights to construct the texture detail enhanced image. Then, we consider the semantic features by putting deep features from the VGG-19 network into a self-attention network, guide the depth decoder network to focus on the integrity of objects in the scene. Finally, we propose a scale-invariant smooth loss to improve the structural integrity of the predicted depth. We evaluate our method on the KITTI 2015 and Make3D datasets and apply the predicted depth to novel view synthesis. The experimental results show that it has achieved satisfactory results compared with the existing methods.