Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss

TL;DR

This paper introduces a novel self-supervised approach for estimating depth and ego-motion from monocular thermal videos, leveraging multi-spectral consistency and a differentiable warping module to operate effectively in low-light conditions.

Contribution

It is the first to simultaneously estimate depth and ego-motion from thermal videos using self-supervised learning with multi-spectral consistency.

Findings

Robust depth and pose estimation in low-light conditions

Effective self-supervised learning framework for thermal videos

First method to combine temperature and photometric consistency for thermal imagery

Abstract

A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory signal by reconstructing clipped and colorized thermal images. Additionally, we design a differentiable forward warping module that can transform the coordinate system of the estimated depth map and relative pose from thermal camera to visible camera. Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks. Networks trained with the proposed method robustly estimate the depth and pose from monocular thermal video under low-light and even zero-light conditions. To the best of our knowledge, this is the first work to simultaneously estimate both depth and ego-motion from monocular thermal video in a self-supervised manner.