ﻻ يوجد ملخص باللغة العربية
Most of the deep-learning based depth and ego-motion networks have been designed for visible cameras. However, visible cameras heavily rely on the presence of an external light source. Therefore, it is challenging to use them under low-light conditions such as night scenes, tunnels, and other harsh conditions. A thermal camera is one solution to compensate for this problem because it detects Long Wave Infrared Radiation(LWIR) regardless of any external light sources. However, despite this advantage, both depth and ego-motion estimation research for the thermal camera are not actively explored until so far. In this paper, we propose an unsupervised learning method for the all-day depth and ego-motion estimation. The proposed method exploits multi-spectral consistency loss to gives complementary supervision for the networks by reconstructing visible and thermal images with the depth and pose estimated from thermal images. The 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 the monocular thermal video in an unsupervised manner.
A new unsupervised learning method of depth and ego-motion using multiple masks from monocular video is proposed in this paper. The depth estimation network and the ego-motion estimation network are trained according to the constraints of depth and e
We propose a semantics-driven unsupervised learning approach for monocular depth and ego-motion estimation from videos in this paper. Recent unsupervised learning methods employ photometric errors between synthetic view and actual image as a supervis
Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised one. It achieves this by using the photometric errors between the targ
Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robots visual perception. We use a joint self-supervised method to estimate the three geometric elements. Depth network, optical flow