Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

• URL: http://arxiv.org/abs/2011.04123v1
• Date: Mon, 9 Nov 2020 01:03:13 GMT
• Title: Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
• Authors: Qing Li, Jiasong Zhu, Jun Liu, Rui Cao, Qingquan Li, Sen Jia, Guoping Qiu
• Abstract summary: Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping. Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques.
• Score: 31.06326714016336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper.

Related papers

• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• DEUX: Active Exploration for Learning Unsupervised Depth Perception [8.044217507775999]
  We develop an active, task-informed, depth uncertainty-based motion planning approach for learning depth completion. We show that our approach further improves zero-shot generalization, while offering new insights into integrating robot learning-based depth estimation.
  arXiv  Detail & Related papers  (2023-09-16T23:33:15Z)
• Self-Supervised Learning based Depth Estimation from Monocular Images [0.0]
  The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input. We plan to do intrinsic camera parameters during training and apply weather augmentations to further generalize our model.
  arXiv  Detail & Related papers  (2023-04-14T07:14:08Z)
• Guided Depth Map Super-resolution: A Survey [88.54731860957804]
  Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image. A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR.
  arXiv  Detail & Related papers  (2023-02-19T15:43:54Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Deep Depth Completion: A Survey [26.09557446012222]
  We provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies. We present a quantitative comparison of model performance on two widely used benchmark datasets, including an indoor and an outdoor dataset.
  arXiv  Detail & Related papers  (2022-05-11T08:24:00Z)
• SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning [53.78813049373321]
  We propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation. Our approach is useful for various applications such as mobile robot navigation and is applicable to diverse environments.
  arXiv  Detail & Related papers  (2022-03-10T12:28:42Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A Survey on Deep Semi-supervised Learning [51.26862262550445]
  We first present a taxonomy for deep semi-supervised learning that categorizes existing methods. We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
  arXiv  Detail & Related papers  (2021-02-28T16:22:58Z)
• WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks [0.0]
  WGANVO is a Deep Learning based monocular Visual Odometry method. In particular, a neural network is trained to regress a pose estimate from an image pair. The method can recover the absolute scale of the scene without neither prior knowledge nor extra information.
  arXiv  Detail & Related papers  (2020-07-27T17:31:24Z)
• Monocular Depth Estimation Based On Deep Learning: An Overview [16.2543991384566]
  Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. Deep learning has been widely studied recently and achieved promising performance in accuracy. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed.
  arXiv  Detail & Related papers  (2020-03-14T12:35:34Z)

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.