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Geometric Constraints in Deep Learning Frameworks: A Survey

Created by
  • Haebom

Author

Vibhas K Vats, David J Crandall

Outline

This paper investigates a deep learning framework that integrates geometric constraints for depth estimation and related vision tasks based on stereophotogrammetry. We introduce the history of stereophotogrammetry and existing shape estimation (Stereo) techniques dating back to the 1800s, and compare and analyze them with approaches that utilize deep learning without geometric modeling. In particular, we present a new taxonomy of geometric constraints used in state-of-the-art deep learning frameworks, and provide insightful observations and future research directions.

Takeaways, Limitations

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Takeaways:
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We provide a comprehensive survey of deep learning frameworks that incorporate geometric constraints.
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We compare and analyze existing geometry-based methods with deep learning-based methods to clearly present the advantages and disadvantages of each method.
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We present a novel taxonomy of geometric constraints used in deep learning frameworks.
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Contributes to the advancement of the field by suggesting future research directions.
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Limitations:
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Further validation of the comprehensiveness and objectivity of the classification scheme presented in the paper may be needed.
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There is a lack of experimental results comparing the performance under different geometric constraints.
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In-depth analysis of specific applications may be lacking.
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