Summary: What You’ve Learned#

Congratulations! You’ve completed the Depth Estimation Guide. Let’s recap what you’ve accomplished and explore where you can go next.

Step-by-Step Recap#

Step 1: Loading Depth Data

You learned how to load depth estimation datasets in FiftyOne, working with both NumPy-based depth maps (DIODE dataset) and image-based depth maps (NYU Depth V2). This included understanding depth validity masks, computing appropriate visualization ranges, and using group_by() to create video-like visualizations from sequential frame data.

Step 2: Using Depth Estimation Models

You explored multiple approaches to running depth estimation models including FiftyOne’s Model Zoo integration, manual Hugging Face integration, community plugins like DepthPro, and the Diffusers library. You learned how to store predictions as Heatmaps and compare multiple model outputs.

Suggested Exercises#

  1. Multi-Model Comparison: Run different depth estimation models on the same dataset and compare their predictions. Which model performs best for indoor vs outdoor scenes?

  2. Custom Dataset Loading: Load your own depth dataset into FiftyOne and structure it with appropriate metadata.

  3. Validity Mask Analysis: For datasets with validity masks, analyze what percentage of pixels have valid depth measurements.

  4. Sequential Grouping: Experiment with different grouping strategies for datasets with sequential frames.

  5. Dataset Curation: Use FiftyOne’s filtering capabilities to curate training datasets by scene type, depth range, or quality characteristics.

Resources and Further Reading#

What to Do Next#

Now that you’ve mastered depth estimation with FiftyOne, here are some suggested next steps:

  • Explore Model Evaluation - Learn how to evaluate depth estimation models by comparing predictions against ground truth and identifying failure cases

  • Try Out FiftyOne Brain - Use similarity analysis to find images with similar depth characteristics or identify outliers in your dataset

  • Build Custom Workflows - Create domain-specific workflows for your depth estimation use case (robotics, AR/VR, autonomous vehicles, 3D reconstruction, etc.)

  • Join the Community - Connect with other FiftyOne users to share insights and learn advanced depth estimation techniques

  • Apply to Real Projects - Use these skills on your production depth estimation datasets to improve data quality and model performance

We’d Love Your Feedback#

Your feedback helps us improve FiftyOne and create better learning experiences. Please let us know:

  • What aspects of this depth estimation guide were most helpful?

  • What could be improved or clarified?

  • What depth estimation-specific topics would you like to see covered in future guides?

  • Any issues or bugs you encountered?

You can reach us at support@voxel51.com or join our Discord community.

Thank you for completing the Depth Estimation Guide! We hope you’re excited to apply these skills to build better depth estimation systems.