3D Reconstruction of Monocular Images based on ResNeXt Neural Network
- DOI
- 10.2991/978-94-6463-540-9_82How to use a DOI?
- Keywords
- 3D Reconstruction; monocular image; ResNeXt neural network
- Abstract
With the rapid advancements in computer vision and image processing technologies, three-dimensional (3D) reconstruction from a single image has emerged as a significant area of research within the field of computer vision. However, due to the inherent lack of depth information in single images, 3D reconstruction tasks still pose numerous challenges. This paper introduces a 3D reconstruction method from a single image based on the ResNeXt neural network, aiming to overcome the limitations of existing technologies and enhance reconstruction accuracy and efficiency. We begin by reviewing relevant technologies in 3D reconstruction and the development of stacked CNNs, with a focus on the architectural features of the ResNeXt network and its performance in image recognition tasks. Subsequently, the proposed 3D reconstruction framework is described in detail, including data preprocessing, model training, and optimization strategies. In the experimental section, the method is comprehensively tested using multiple public datasets. The results indicate that our approach outperforms current mainstream 3D reconstruction algorithms on several performance metrics, particularly in handling complex scenes and texture details. Finally, the paper discusses the experimental outcomes, analyzes the strengths of the method and the challenges it currently faces, and explores future research directions.
- Copyright
- © 2024 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
TY - CONF AU - Yu Zhang PY - 2024 DA - 2024/10/16 TI - 3D Reconstruction of Monocular Images based on ResNeXt Neural Network BT - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024) PB - Atlantis Press SP - 816 EP - 829 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-540-9_82 DO - 10.2991/978-94-6463-540-9_82 ID - Zhang2024 ER -