The fusion strategy of 2d and 3d information based on deep learning: A review

9Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

Abstract

Recently, researchers have realized a number of achievements involving deep-learning-based neural networks for the tasks of segmentation and detection based on 2D images, 3D point clouds, etc. Using 2D and 3D information fusion for the advantages of compensation and accuracy improvement has become a hot research topic. However, there are no critical reviews focusing on the fusion strategies of 2D and 3D information integration based on various data for segmentation and detection, which are the basic tasks of computer vision. To boost the development of this research domain, the existing representative fusion strategies are collected, introduced, categorized, and summarized in this paper. In addition, the general structures of different kinds of fusion strategies were firstly abstracted and categorized, which may inspire researchers. Moreover, according to the methods included in this paper, the 2D information and 3D information of different methods come from various kinds of data. Furthermore, suitable datasets are introduced and comparatively summarized to support the relative research. Last but not least, we put forward some open challenges and promising directions for future research.

Cite

CITATION STYLE

APA

Zhao, J., Wang, Y., Cao, Y., Guo, M., Huang, X., Zhang, R., … Wang, J. (2021, October 1). The fusion strategy of 2d and 3d information based on deep learning: A review. Remote Sensing. MDPI. https://doi.org/10.3390/rs13204029

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free