Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation

TL;DR

This paper introduces a structure-centric approach with knowledge distillation to improve the robustness of monocular depth estimation in adverse real-world conditions, leveraging semantic and structural cues.

Contribution

It proposes a novel method that reduces reliance on local textures and incorporates semantic structural knowledge via a teacher-student framework with learnable graphs.

Findings

Achieves state-of-the-art out-of-distribution depth estimation performance.

Enhances robustness against interference textures and poor lighting conditions.

Demonstrates scalability and compatibility across various datasets.

Abstract

Monocular depth estimation, enabled by self-supervised learning, is a key technique for 3D perception in computer vision. However, it faces significant challenges in real-world scenarios, which encompass adverse weather variations, motion blur, as well as scenes with poor lighting conditions at night. Our research reveals that we can divide monocular depth estimation into three sub-problems: depth structure consistency, local texture disambiguation, and semantic-structural correlation. Our approach tackles the non-robustness of existing self-supervised monocular depth estimation models to interference textures by adopting a structure-centered perspective and utilizing the scene structure characteristics demonstrated by semantics and illumination. We devise a novel approach to reduce over-reliance on local textures, enhancing robustness against missing or interfering patterns. Additionally, we incorporate a semantic expert model as the teacher and construct inter-model feature dependencies via learnable isomorphic graphs to enable aggregation of semantic structural knowledge. Our approach achieves state-of-the-art out-of-distribution monocular depth estimation performance across a range of public adverse scenario datasets. It demonstrates notable scalability and compatibility, without necessitating extensive model engineering. This showcases the potential for customizing models for diverse industrial applications.