Summary of Gvdepth: Zero-shot Monocular Depth Estimation For Ground Vehicles Based on Probabilistic Cue Fusion, by Karlo Koledic et al.
GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion
by Karlo Koledic, Luka Petrovic, Ivan Markovic, Ivan Petrovic
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 A novel approach is presented to generalize metric monocular depth estimation by proposing a canonical representation that disentangles depth from camera parameters. This allows for accurate estimation across various datasets and camera setups, which is crucial in autonomous vehicles and mobile robotics where data collection is often limited. The proposed method combines object size and vertical image position cues using an adaptive probabilistic fusion architecture. Evaluation on five autonomous driving datasets demonstrates the effectiveness of the approach, achieving comparable accuracy to existing zero-shot methods while training on a single dataset with a single-camera setup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers understand how far things are from them is developed. This method works well even when using cameras that are set up in different ways, which is important for self-driving cars and robots that move around. The approach uses two different cues: the size of objects and where they appear in an image. These cues are combined using a special algorithm that adapts to different situations. The method is tested on five different datasets and performs well, even when compared to other methods that don’t need any training data. |
Keywords
» Artificial intelligence » Depth estimation » Zero shot
