Edge-Guided Monocular Absolute Depth Estimation with Diffusion-Based Refinement

Monocular Depth Estimation (MDE) models have shown significant potential in estimating depth from single images. However, the resulting depth maps occasionally lack fine-grained details, blurred edges, and missing objects, limiting their practical utility. To solve those limitations, we propose a dual-encoder model with a feature fusion module - FFM, a customised loss function, and a depth-based diffusion module to enhance predicted depth maps. Our model integrates edge information by incorporating an edge encoder that utilises edge data and an RGB feature extractor followed by an FFM module to fuse the information from both encoders. The edge loss function is used to evaluate the accuracy of edge outputs generated using the Scharr filter to improve edge detection in depth maps. As most current MDE studies predict depth maps at half the spatial resolution, we developed a depth-based diffusion module to generate higher-resolution depth maps, preserving fine details. Our approach demonstrates state-of-the-art performance, with precise edge capture, when evaluated on the KITTI and NYU-v2 datasets.