Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
| Autor: | Ukcheol Shin, Kyunghyun Lee, Byeong-Uk Lee, In So Kweon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Control and Optimization Mechanical Engineering Computer Vision and Pattern Recognition (cs.CV) Biomedical Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Computer Science Applications Human-Computer Interaction Computer Science - Robotics Artificial Intelligence Control and Systems Engineering Computer Science::Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Robotics (cs.RO) |
| Popis: | 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. 8 pages, Accepted by IEEE Robotics and Automation Letters (RA-L) with IROS 2022 option |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|