What is it about?

Stereophotogrammetry is a long-standing method for understanding scenes from images, dating back to the 1800s when people began using photographs to measure the real world. Over time, many techniques have been developed. A traditional approach, called Shape from Stereo, uses geometry to figure out how scenes and cameras relate. In contrast, deep learning methods often skip explicitly modeling geometry. This survey looks at deep learning approaches that are inspired by geometry. It compares how different methods include geometric constraints in tasks like depth estimation. The paper introduces a new way to categorize these methods and shares key insights along with ideas for future research.

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Why is it important?

This kind of work is crucial because it bridges the gap between traditional geometric approaches and modern deep learning techniques. Incorporating geometric constraints into deep learning models enhances their ability to understand and interpret complex visual scenes more accurately. This integration leads to more robust and reliable computer vision systems, which are essential in applications such as autonomous driving, robotics, and augmented reality. By providing a structured analysis and highlighting future research avenues, your survey serves as a valuable resource for researchers and practitioners aiming to develop advanced vision systems that leverage both geometric understanding and deep learning capabilities.

Perspectives

From my perspective, there remains a significant gap between how geometric constraints were traditionally used in classical computer vision and how they are currently incorporated into learning-based methods. Classical approaches, such as multi-view stereo or structure-from-motion, were deeply rooted in precise mathematical formulations of geometry, treating constraints like epipolar consistency, depth continuity, and rigid motion as fundamental to solving vision problems. These methods relied on interpretable, physically grounded models with strong geometric priors. In contrast, many modern deep learning frameworks tend to treat geometry as an implicit byproduct of data-driven learning, often relying on large datasets to compensate for the lack of structured priors. While this has led to impressive performance gains, it also introduces issues of generalization, interpretability, and robustness. I believe it's time we re-emphasize the importance of geometric constraints—integrating them more explicitly into learning pipelines—not only to bridge this gap but to build models that are more accurate, efficient, and physically consistent.

Vibhas Kumar Vats
Indiana University Bloomington

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This page is a summary of: Geometric Constraints in Deep Learning Frameworks: A Survey, ACM Computing Surveys, April 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3729221.
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