3,085 research outputs found
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu
- …