Summary of Doubletake: Geometry Guided Depth Estimation, by Mohamed Sayed and Filippo Aleotti and Jamie Watson and Zawar Qureshi and Guillermo Garcia-hernando and Gabriel Brostow and Sara Vicente and Michael Firman
DoubleTake: Geometry Guided Depth Estimation
by Mohamed Sayed, Filippo Aleotti, Jamie Watson, Zawar Qureshi, Guillermo Garcia-Hernando, Gabriel Brostow, Sara Vicente, Michael Firman
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed model for estimating depth from posed RGB images leverages historical predictions by incorporating the latest 3D geometry data as an extra input. This approach enables the model to encode information from areas not covered by keyframes, resulting in a more regularized estimate. The Hint MLP combines cost volume features with the prior geometry hint and confidence measure. Our method achieves state-of-the-art results in offline and incremental evaluation scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our research uses computer vision to estimate depth from posed images. We show that giving our model information about what it predicted earlier helps it make better guesses about what’s behind each camera view. This approach lets us get more accurate estimates of how far away objects are, even when there are areas not covered by previous views. |
