  Recently, self-supervised learning of depth and ego-motion from thermal
images shows strong robustness and reliability under challenging scenarios.
However, the inherent thermal image properties such as weak contrast, blurry
edges, and noise hinder to generate effective self-supervision from thermal
images. Therefore, most research relies on additional self-supervision sources
such as well-lit RGB images, generative models, and Lidar information. In this
paper, we conduct an in-depth analysis of thermal image characteristics that
degenerates self-supervision from thermal images. Based on the analysis, we
propose an effective thermal image mapping method that significantly increases
image information, such as overall structure, contrast, and details, while
preserving temporal consistency. The proposed method shows outperformed depth
and pose results than previous state-of-the-art networks without leveraging
additional RGB guidance.
