Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However, lacking a specific mechanism to make the network learn more about the regions containing moving objects or occlusion scenarios, existing depth estimation methods likely produce poor results for them. Therefore, we propose an uncertainty quantification method to improve the performance of existing depth estimation networks without changing their architectures. Our uncertainty quantification method consists of uncertainty measurement, the learning guidance by uncertainty, and the ultimate adaptive determination. Firstly, with Snapshot and Siam learning strategies, we measure the uncertainty degree by calculating the variance of pre-converged epochs or twins during training. Secondly, we use the uncertainty to guide the network to strengthen learning about those regions with more uncertainty. Finally, we use the uncertainty to adaptively produce the final depth estimation results with a balance of accuracy and robustness. To demonstrate the effectiveness of our uncertainty quantification method, we apply it to two state-of-the-art models, Monodepth2 and Hints. Experimental results show that our method has improved the depth estimation performance in seven evaluation metrics compared with two baseline models and exceeded the existing uncertainty method.

Self-supervised monocular depth estimation has been widely investigated and applied in previous works. However, existing methods suffer from texture-copy, depth drift, and incomplete structure. It is difficult for normal CNN networks to completely understand the relationship between the object and its surrounding environment. Moreover, it is hard to design the depth smoothness loss to balance depth smoothness and sharpness. To address these issues, we propose a coarse-to-fine method with a normalized convolutional block attention module (NCBAM). In the coarse estimation stage, we incorporate the NCBAM into depth and pose networks to overcome the texture-copy and depth drift problems. Then, we use a new network to refine the coarse depth guided by the color image and produce a structure-preserving depth result in the refinement stage. Our method can produce results competitive with state-of-the-art methods. Comprehensive experiments prove the effectiveness of our two-stage method using the NCBAM.