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A Comprehensive Review of Vision-Based 3D Reconstruction Methods
Linglong Zhou1, Guoxin Wu1, Yunbo Zuo1
1Key Laboratory of Modern Measurement and Control Technology Ministry of Education, Beijing Information Science and Technology University, Beijing 100080, China.
Sensors (Basel, Switzerland)
|April 13, 2024
Summary
This survey explores 3D reconstruction methods, including traditional static, dynamic, and machine learning approaches. It provides a comprehensive overview of current trends and challenges in this rapidly advancing field.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Graphics
Background:
- 3D reconstruction is a rapidly developing field, driven by algorithms like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS).
- This technology is crucial for training computer vision models and advancing artificial intelligence.
- The demand for high-precision and efficient 3D reconstruction is growing across various applications, including autonomous systems, HCI, VR, and medicine.
Purpose of the Study:
- To categorize and compare diverse 3D reconstruction methods.
- To provide a comprehensive overview of traditional static, dynamic, and machine learning approaches.
- To analyze current trends and challenges in 3D reconstruction research.
Main Methods:
- Categorization of 3D reconstruction techniques into traditional static, dynamic, and machine learning.
- Comparative analysis of different 3D reconstruction methods.
- Review of emerging algorithms such as NeRF and 3DGS.
Main Results:
- A structured classification of 3D reconstruction technologies.
- Insights into the strengths and weaknesses of various approaches.
- Identification of key trends shaping the future of 3D reconstruction.
Conclusions:
- 3D reconstruction is an inevitable area of development with significant implications for AI and various industries.
- The survey offers a foundational understanding for researchers entering the field.
- Continued advancements in deep learning and GPU technology will further drive the demand for sophisticated 3D reconstruction solutions.
