Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Cross-Domain Applications of Multi-Dimensional Data Alignment Technology

Authors
Fengshuo Kou1, *
1School of Computer and Control Engineering, Electronic Information Category, Northeast Forestry University, Harbin, Heilongjiang, China
*Corresponding author. Email: kfs60113@outlook.com
Corresponding Author
Fengshuo Kou
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_26How to use a DOI?
Keywords
2D and 3D Data Alignment; Cross-Domain Application; Autonomous Driving; Robot Navigation; Augmented Reality
Abstract

Against the backdrop of the rapid development of intelligent technology, cross-modal data fusion is driving innovations across various fields. As a key supporting technology, 2D and 3D data alignment technology is becoming increasingly prominent in value. 2D data (e.g., camera images) is rich in semantic and visual details but lacks spatial depth, while 3D data (e.g., LiDAR point clouds) can accurately represent the spatial structure of objects yet has sparse semantic information. This paper focuses on this alignment technology, conducting in-depth research on its application scenarios, implementation methods, experimental results, and literature support in five fields: autonomous driving, robot navigation, augmented reality, indoor scene construction, and medicine. Through comparative analysis, the advantages, disadvantages, and common challenges of the technology are clarified, and future development directions are proposed. This research provides reference and support for optimizing cross-modal tasks in various fields. This article aims to promote practical innovation and long-term development of intelligent technology.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_26How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Fengshuo Kou
PY  - 2026
DA  - 2026/04/24
TI  - Cross-Domain Applications of Multi-Dimensional Data Alignment Technology
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 233
EP  - 241
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_26
DO  - 10.2991/978-94-6239-648-7_26
ID  - Kou2026
ER  -