Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance

• URL: http://arxiv.org/abs/2011.10697v4
• Date: Fri, 12 Nov 2021 16:54:23 GMT
• Title: Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance
• Authors: Elhousni Mahdi, Zhang Ziming and Huang Xinming
• Abstract summary: This letter proposes a two-stage approach, where first a multi-task neural network is used to predict the height map resulting from a single RGB aerial input image. Experiments on two publicly available datasets show that our method is capable of producing state-of-the-art results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment of deep learning which replaced old multi-view geometry techniques. This letter proposes a two-stage approach, where first a multi-task neural network is used to predict the height map resulting from a single RGB aerial input image. We also include a second refinement step, where a denoising autoencoder is used to produce higher quality height maps. Experiments on two publicly available datasets show that our method is capable of producing state-of-the-art results. Code is available at https://github.com/melhousni/DSMNet.

Related papers

• DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features [65.8738034806085]
  DistillNeRF is a self-supervised learning framework for understanding 3D environments in autonomous driving. It predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs. It is trained self-supervised with differentiable rendering to reconstruct RGB, depth, or feature images.
  arXiv  Detail & Related papers  (2024-06-17T21:15:13Z)
• Neural Implicit Dense Semantic SLAM [83.04331351572277]
  We propose a novel RGBD vSLAM algorithm that learns a memory-efficient, dense 3D geometry, and semantic segmentation of an indoor scene in an online manner. Our pipeline combines classical 3D vision-based tracking and loop closing with neural fields-based mapping. Our proposed algorithm can greatly enhance scene perception and assist with a range of robot control problems.
  arXiv  Detail & Related papers  (2023-04-27T23:03:52Z)
• GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs [49.55919802779889]
  We propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. In this work, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. Our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps.
  arXiv  Detail & Related papers  (2022-10-19T17:56:03Z)
• A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction [0.0]
  Current software functions optimally only on near-nadir images, though off-nadir images are often the first sources of information following a natural disaster. This study proposes a deep learning ensemble framework to predict geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of cities worldwide.
  arXiv  Detail & Related papers  (2022-05-04T08:33:41Z)
• Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion [56.85837052421469]
  Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion.
  arXiv  Detail & Related papers  (2021-03-30T21:22:26Z)
• Deep Gradient Projection Networks for Pan-sharpening [20.929492740317915]
  This paper develops a model-based deep pan-sharpening approach. By stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.
  arXiv  Detail & Related papers  (2021-03-08T07:51:58Z)
• Adaptive Context-Aware Multi-Modal Network for Depth Completion [107.15344488719322]
  We propose to adopt the graph propagation to capture the observed spatial contexts. We then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks.
  arXiv  Detail & Related papers  (2020-08-25T06:00:06Z)
• Height estimation from single aerial images using a deep ordinal regression network [12.991266182762597]
  We deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem.
  arXiv  Detail & Related papers  (2020-06-04T12:03:51Z)
• Towards Reading Beyond Faces for Sparsity-Aware 4D Affect Recognition [55.15661254072032]
  We present a sparsity-aware deep network for automatic 4D facial expression recognition (FER) We first propose a novel augmentation method to combat the data limitation problem for deep learning. We then present a sparsity-aware deep network to compute the sparse representations of convolutional features over multi-views.
  arXiv  Detail & Related papers  (2020-02-08T13:09:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.