Leveraging Depth Foundation Models in Self Supervised Monocular Depth Estimation

Monocular depth estimation is a critical component of autonomous driving, enabling vehicles to perceive and navigate their surroundings efficiently. Existing self-supervised methods often rely on complicated architectures and complex loss functions and face major challenges like inaccurate depth estimation for dynamic objects, blurred edge boundary, and edge flattening [1]. In this paper, we introduce a novel approach to leverage powers of high quality pseudo-depth supervision from DepthAnything-V2 model [2] to existing self-supervised approaches. We propose a novel dynamic mask generation method for any kind of dynamic objects including non-rigid objects in the scene, without complicated training procedures and loss functions. The simplicity of our approach, with competitive results compared to existing methods that rely on sophisticated techniques to solve basic problems, provides a robust and practical solution for real-world deployment. Extensive experiments on Cityscapes datasets verify the effectiveness of our method and establish it as a competitive alternative to existing monocular depth estimation models.