Unsupervised monocular depth estimation based on local planar guidance layer

As computer vision technology advances, depth estimation has garnered significant interest for addressing depth perception challenges in the field. However, traditional unsupervised monocular infrared image depth estimation methods have difficulty dealing with issues such as edge blurriness and occlusions, resulting in poor depth estimation performance. Therefore, this paper proposes an enhanced DispNet network with local planar guidance layer for unsupervised monocular infrared image depth estimation. Firstly, it addresses the problems of edge blurriness and occluded objects in infrared images through the utilization of multiple small-resolution grayscale blocks and multi-scale feature fusion. Secondly, a plane constraint is introduced on the depth map in the local planar guidance layer to reduce noise and discontinuities in the depth map. On the infrared image dataset Iray, the Abs Rel and RMS log were 0.262 and 0.332, respectively, with an accuracy rate of 94.5% when the threshold metric was less than 1.25³ . The experimental results reveal that the algorithm achieves the expected effect on infrared images, and can provide more reliable visual perception performance when driving at night.