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Physics-inspired self-learning framework for unsupervised depth estimation on non-Lambertian surfaces

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This framework uncovers depth in images on non-Lambertian surfaces, suggesting improved accuracy and robustness.

The proposed framework improves depth estimation accuracy by addressing non-Lambertian surfaces and ghosting artifacts.
Achieving average improvements of 9.29% and 2.86% on Sq Rel and RMSE metrics respectively, the model shows significant performance gains.
Utilizing depth consistency loss and a Multi-Path Transformer, the approach combines photometric assumptions and game-theoretic strategies.
The method also displays strong zero-shot generalization capabilities, indicating its versatility across different datasets.

Li et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This framework uncovers depth in images on non-Lambertian surfaces, suggesting improved accuracy and robustness.

The proposed framework improves depth estimation accuracy by addressing non-Lambertian surfaces and ghosting artifacts.
Achieving average improvements of 9.29% and 2.86% on Sq Rel and RMSE metrics respectively, the model shows significant performance gains.
Utilizing depth consistency loss and a Multi-Path Transformer, the approach combines photometric assumptions and game-theoretic strategies.
The method also displays strong zero-shot generalization capabilities, indicating its versatility across different datasets.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark