A real-time unsupervised monocular depth estimation method for outdoor scenes

Mainstream monocular depth estimation methods generally excel in accuracy but fall short in runtime performance. The main challenge in improving computational efficiency is to reduce the computation complexity and memory usage. Addressing this issue, we innovate an unsupervised monocular depth estimation method that not only achieves great real-time performance but also maintain high accuracy. We present a lightweight depth estimation network that leverages inverted residuals. Besides, we build a training scheme with multiple effective loss functions. Experimental validation on KITTI dataset demonstrates that our method not only rivals mainstream models in terms of accuracy but also exhibits lower number of parameters and FLOPs.