论文标题
从热图像中最大化自我监督，以有效地自我监督深度学习和自我运动
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
论文作者
论文摘要
最近，在充满挑战的情况下，对热图像的深度和自我动机的自我监督学习表现出强大的鲁棒性和可靠性。但是，固有的热图像属性，例如弱对比度，模糊边缘和噪声阻碍，从而从热图像中产生有效的自学意义。因此，大多数研究依赖于其他自我审视的来源，例如光线充足的RGB图像，生成模型和LiDAR信息。在本文中，我们对热图像特征进行了深入的分析，该特征从热图像中解脱出来。基于分析，我们提出了一种有效的热图像映射方法，该方法可显着增加图像信息，例如总体结构，对比度和细节，同时保留时间一致性。所提出的方法比以前的最先进的网络显示出胜过深度和姿势结果，而无需利用其他RGB指导。
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.
