Better depth and motion from thermal images by improving self-supervised learning
Better depth and motion from thermal images by improving self-supervised learning
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
arXiv paper abstract https://arxiv.org/abs/2201.04387v1
arXiv PDF paper https://arxiv.org/pdf/2201.04387v1.pdf
... self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios.
However ... thermal image properties such as weak contrast, blurry edges, and noise hinder to generate effective self-supervision
... Therefore, most research relies on additional self-supervision sources such as well-lit RGB images, generative models, and Lidar information.
... conduct an in-depth analysis of thermal image characteristics that degenerates self-supervision from thermal images.
... propose ... thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency.
... shows outperformed depth and pose results than previous state-of-the-art networks without leveraging additional RGB guidance.
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