Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets

Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work pro...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-09, Vol.24 (18), p.5861
Main Author: Lee, Yangming
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
dataset
Datasets
Deep learning
Digital cameras
Geometry
Image processing
Localization
Machine learning
Machine vision
Methods
Neural networks
Optimization techniques
Partial differential equations
Photogrammetry
Review
Robotics
three-dimensional dense reconstruction
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Summary:Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24185861