Integrating convolutional guidance and Transformer fusion with Markov Random Fields smoothing for monocular depth estimation

Monocular depth estimation is a challenging and prominent problem in current computer vision research and is widely used in intelligent transportation like environment perception, navigation and localization. Accurately delineating object boundaries and ensuring smooth transitions in estimated depth images from a single image remain significant challenges. These issues place higher demands on the network's global and local feature extraction capabilities. In response, we proposed a depth estimation framework, designed to address detection accuracy and the global smooth transition of predicted depth maps. Our method introduces a novel feature decoding structure named Convolutional Guided Fusion (CoGF), which utilizes local features extracted by a convolutional neural network as a guide and fuses them with long-range dependent features extracted by a Transformer. This approach enables the model to retain both local details and global contextual information during the decoding process. To ensure global smoothness in the depth estimation results, we incorporate a smoothing strategy based on Markov Random Fields (MRF), enhancing pixel-to-pixel continuity and ensuring robust spatial consistency in the generated depth maps. Our proposed method is evaluated on current mainstream benchmarks. Experimental results demonstrate that our depth estimation method outperforms previous approaches. The code is available at https://github.com/pxrw/CGTF-Depth.git.