Summary of Monopp: Metric-scaled Self-supervised Monocular Depth Estimation by Planar-parallax Geometry in Automotive Applications, By Gasser Elazab et al.
MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications
by Gasser Elazab, Torben Gräber, Michael Unterreiner, Olaf Hellwich
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 This paper proposes a novel self-supervised approach to monocular depth estimation (MDE) that requires only video data from vehicles and the camera’s mounting position. The model uses planar-parallax geometry to reconstruct scene structure, consisting of three networks: multi-frame, single-frame, and pose networks. The multi-frame network processes sequential frames to estimate static scene structure, which is then used to train the single-frame network for scale-invariant depth prediction. The pose network predicts relative poses between images. The method achieves state-of-the-art results on KITTI and demonstrates its effectiveness on Cityscapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better predict depths from car cameras without needing extra information. It presents a new way to do this using only video data and the camera’s position. This approach uses special geometry to figure out what parts of the scene are not moving, which helps it learn about scales and distances. The model does well on real-world tests and shows promise for future applications. |
Keywords
» Artificial intelligence » Depth estimation » Self supervised
