Summary of Depth Estimation Using Weighted-loss and Transfer Learning, by Muhammad Adeel Hafeez et al.
Depth Estimation using Weighted-loss and Transfer Learning
by Muhammad Adeel Hafeez, Michael G. Madden, Ganesh Sistu, Ihsan Ullah
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a simplified approach to improve depth estimation from 2D images using transfer learning and an optimized loss function. The optimized loss function combines weighted losses for Mean Absolute Error (MAE), Edge Loss, and Structural Similarity Index (SSIM) to enhance robustness and generalization. The authors use grid search and random search methods to find optimal weights, resulting in improved model performance. Various encoder-decoder-based models are explored on the NYU Depth Dataset v2, with EfficientNet pre-trained on ImageNet yielding the best results for RSME, REL, and log10 metrics. A qualitative analysis demonstrates that the proposed model produces accurate depth maps even when ground truth is flawed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve depth estimation from 2D images, which has many real-world applications. The authors suggest a new way to make this process more accurate by combining different loss functions and using pre-trained models. They test their idea on a specific dataset and find that it works well. This could be useful for things like self-driving cars or robots. |
Keywords
» Artificial intelligence » Depth estimation » Encoder decoder » Generalization » Grid search » Loss function » Mae » Transfer learning
