Deep learning approach has achieved great success in monocular depth estimation. However, the learned deep network may produce a depth map with fewer details and incorrect global depth layout, especially when the learned network is applied to a high-resolution image. In order to generate a high-quality depth map with better global structure and richer details, we propose a multi-scale residual Laplacian pyramid fusion net (MS-RLap-FNet), to fuse the multi-scale depth maps estimated by the existing depth estimation models, for depth refinement. Our approach relies on a proposed multi-scale residual Laplacian pyramid decomposition of the multi-scale depth maps, and the fusion network modules to gradually refine the depth maps based on the decomposition from low to high resolution. Comprehensive experiments show that our method, by refining the depth maps based on three popular monocular depth estimation models (DPT, MiDas, SGR), outperforms the existing state-of-the-art methods both in quantity and quality on three public datasets with different image resolutions. The depth map refined by our method has better global depth layout with richer fine details.

Estimating depth and defocus maps are two fundamental tasks in computer vision. Recently, many methods explore these two tasks separately with the help of the powerful feature learning ability of deep learning and these methods have achieved impressive progress. However, due to the difficulty in densely labeling depth and defocus on real images, these methods are mostly based on synthetic training dataset, and the performance of learned network degrades significantly on real images. In this paper, we tackle a new task that jointly estimates depth and defocus from a single image. We design a dual network with two subnets respectively for estimating depth and defocus. The network is jointly trained on synthetic dataset with a physical constraint to enforce the physical consistency between depth and defocus. Moreover, we design a simple method to label depth and defocus order on real image dataset, and design two novel metrics to measure accuracies of depth and defocus estimation on real images. Comprehensive experiments demonstrate that joint training for depth and defocus estimation using physical consistency constraint enables these two subnets to guide each other, and effectively improves their depth and defocus estimation performance on real defocused image dataset.