What question did this study set out to answer?

This review aims to analyze the advancements in image-based 3D reconstruction, focusing on both conventional photogrammetric techniques and learning-based methods.

August 13, 2026PFG – Journal of Photogrammetry Remote Sensing and Geoinformation ScienceOpen Access

Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques

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Implication

Review demonstrates techniques for 3D reconstruction in various applications, indicating a paradigm shift in approaches.

Key Points

This review aims to analyze the advancements in image-based 3D reconstruction, focusing on both conventional photogrammetric techniques and learning-based methods.
Systematic review of traditional photogrammetric techniques like Structure from Motion (SfM) and Multi-View Stereo (MVS).
Exploration of recent innovations in scalable SfM and specialized camera models for MVS.
Discussion on learning-based methods, including deep SfM and neural rendering approaches.
Highlighted the need for learning-based methods to ensure metric accuracy and reliability alongside visual realism.
Outlined challenges in generalization across domains and real-time performance for high-resolution imagery.

Cite This Study

A 2026 study studied this question.

synapsesocial.com/papers/6a7d76172b0e0cff3f63f2c8 https://doi.org/10.1007/s41064-026-00412-y

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Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques

Discussion

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Implication

Key Points

Cite This Study

Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

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