Mining Scene Structural Guidance for Thermal Images in Self-Supervised Monocular Depth Estimation

Abstract

Self-supervised monocular depth estimation from RGB images has seen significant advancements recently, primarily because it eliminates the need for ground truth data during training. However, applying this technique to thermal images remains challenging due to their inherent characteristics, such as low contrast, low texture, and low signal-to-noise ratio, which impede accurate self-supervision. In this paper, we propose leveraging reliable and distinct scene structural information from thermal images to enhance self-supervised signals. We introduce structural losses, including explicit structural loss in the image space and implicit structural loss in the feature space, to improve self-supervised depth estimation. This approach mitigates the interference caused by the degraded characteristics of thermal images. Our method demonstrates superior performance compared to previous state-of-the-art approaches on the ViViD benchmark dataset, both quantitatively and qualitatively.