scholarly journals MULTI-TASK LEARNING FROM FIXED-WING UAV IMAGES FOR 2D/3D CITY MODELLING

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 1-5
Author(s):  
M. R. Bayanlou ◽  
M. Khoshboresh-Masouleh
Keyword(s):  
Scene Understanding ◽  
Depth Estimation ◽  
Traffic Monitoring ◽  
Training Data ◽  
Specific Information ◽  
Infrastructure Development ◽  
Task Learning ◽  
Monocular Depth ◽  
Uav Images ◽  
3D City Modelling

Abstract. Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information contained in the training data of related tasks. In urban management applications such as infrastructure development, traffic monitoring, smart 3D cities, and change detection, automated multi-task data analysis for scene understanding based on the semantic, instance, and panoptic annotation, as well as monocular depth estimation, is required to generate precise urban models. In this study, a common framework for the performance assessment of multi-task learning methods from fixed-wing UAV images for 2D/3D city modelling is presented.

scholarly journals DEEP LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM UAV IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 451-458
Author(s):  
L. Madhuanand ◽  
F. Nex ◽  
M. Y. Yang
Keyword(s):  
Deep Learning ◽  
Ground Level ◽  
Depth Estimation ◽  
Aerial Images ◽  
Aerial Image ◽  
Depth Information ◽  
Single Image ◽  
Monocular Depth ◽  
Uav Images ◽  
Image Depth

Abstract. Depth is an essential component for various scene understanding tasks and for reconstructing the 3D geometry of the scene. Estimating depth from stereo images requires multiple views of the same scene to be captured which is often not possible when exploring new environments with a UAV. To overcome this monocular depth estimation has been a topic of interest with the recent advancements in computer vision and deep learning techniques. This research has been widely focused on indoor scenes or outdoor scenes captured at ground level. Single image depth estimation from aerial images has been limited due to additional complexities arising from increased camera distance, wider area coverage with lots of occlusions. A new aerial image dataset is prepared specifically for this purpose combining Unmanned Aerial Vehicles (UAV) images covering different regions, features and point of views. The single image depth estimation is based on image reconstruction techniques which uses stereo images for learning to estimate depth from single images. Among the various available models for ground-level single image depth estimation, two models, 1) a Convolutional Neural Network (CNN) and 2) a Generative Adversarial model (GAN) are used to learn depth from aerial images from UAVs. These models generate pixel-wise disparity images which could be converted into depth information. The generated disparity maps from these models are evaluated for its internal quality using various error metrics. The results show higher disparity ranges with smoother images generated by CNN model and sharper images with lesser disparity range generated by GAN model. The produced disparity images are converted to depth information and compared with point clouds obtained using Pix4D. It is found that the CNN model performs better than GAN and produces depth similar to that of Pix4D. This comparison helps in streamlining the efforts to produce depth from a single aerial image.

scholarly journals SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5476
Author(s):  
Rui Wang ◽  
Jialing Zou ◽  
James Zhiqing Wen
Keyword(s):  
Network Architecture ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Semantic Feature ◽  
Semantic Features ◽  
Feature Maps ◽  
Task Learning ◽  
Vision Sensors ◽  
Monocular Depth ◽  
Public Datasets

Monocular depth estimation based on unsupervised learning has attracted great attention due to the rising demand for lightweight monocular vision sensors. Inspired by multi-task learning, semantic information has been used to improve the monocular depth estimation models. However, multi-task learning is still limited by multi-type annotations. As far as we know, there are scarcely any large public datasets that provide all the necessary information. Therefore, we propose a novel network architecture Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) to extract multi-resolution depth features and semantic features, which are merged and fed into the decoder, with the goal of predicting depth with the support of semantics. Instead of using loss functions to relate the semantics and depth, the fusion of feature maps for semantics and depth is employed to predict the monocular depth. Therefore, two accessible datasets with similar topics for depth estimation and semantic segmentation can meet the requirements of SFA-MDEN for training sets. We explored the performance of the proposed SFA-MDEN with experiments on different datasets, including KITTI, Make3D, and our own dataset BHDE-v1. The experimental results demonstrate that SFA-MDEN achieves competitive accuracy and generalization capacity compared to state-of-the-art methods.

scholarly journals SELF-SUPERVISED LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM AERIAL IMAGERY

2020 ◽  
Vol V-2-2020 ◽  
pp. 357-364
Author(s):  
M. Hermann ◽  
B. Ruf ◽  
M. Weinmann ◽  
S. Hinz
Keyword(s):  
Supervised Learning ◽  
Image Matching ◽  
Ground Truth ◽  
Depth Estimation ◽  
Training Data ◽  
Aerial Imagery ◽  
Small Model ◽  
Conventional Methods ◽  
Monocular Depth ◽  
Real Time Application

Abstract. Supervised learning based methods for monocular depth estimation usually require large amounts of extensively annotated training data. In the case of aerial imagery, this ground truth is particularly difficult to acquire. Therefore, in this paper, we present a method for self-supervised learning for monocular depth estimation from aerial imagery that does not require annotated training data. For this, we only use an image sequence from a single moving camera and learn to simultaneously estimate depth and pose information. By sharing the weights between pose and depth estimation, we achieve a relatively small model, which favors real-time application. We evaluate our approach on three diverse datasets and compare the results to conventional methods that estimate depth maps based on multi-view geometry. We achieve an accuracy δ1:25 of up to 93.5 %. In addition, we have paid particular attention to the generalization of a trained model to unknown data and the self-improving capabilities of our approach. We conclude that, even though the results of monocular depth estimation are inferior to those achieved by conventional methods, they are well suited to provide a good initialization for methods that rely on image matching or to provide estimates in regions where image matching fails, e.g. occluded or texture-less regions.

Unsupervised Monocular Depth Estimation for Autonomous Driving

2019 ◽  
pp. 128
Author(s):  
Chih-Shuan Huang ◽  
Wan-Nung Tsung ◽  
Wei-Jong Yang ◽  
Chin-Hsing Chen
Keyword(s):  
Depth Estimation ◽  
Autonomous Driving ◽  
Monocular Depth

Hierarchical Object Relationship Constrained Monocular Depth Estimation.

2021 ◽  
pp. 108116
Author(s):  
Shuai Li ◽  
Jiaying Shi ◽  
Wenfeng Song ◽  
Aimin Hao ◽  
Hong Qin
Keyword(s):  
Depth Estimation ◽  
Monocular Depth ◽  
Object Relationship
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