Promoting Monocular Depth Estimation by Multi-Scale Residual Laplacian Pyramid Fusion

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.