Summary of Adaptive Discrete Disparity Volume For Self-supervised Monocular Depth Estimation, by Jianwei Ren
Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth Estimation
by Jianwei Ren
First submitted to arxiv on: 4 Apr 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 The proposed Adaptive Discrete Disparity Volume (ADDV) module learns to dynamically sense depth distributions in different RGB images and generate adaptive bins for them. This self-supervised approach is integrated into existing CNN architectures, allowing networks to produce representative values for bins and a probability volume over them. The ADDV module is trained using novel strategies that provide regularizations under self-supervised conditions, preventing model degradation or collapse. Empirical results show that ADDV effectively processes global information, generating appropriate bins for various scenes and producing higher quality depth maps compared to handcrafted methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can create accurate 3D images from just a single photo without any special labels. The researchers developed a new way to divide the distance between objects in a scene into smaller, more meaningful groups. This approach allows computers to learn and improve on their own, making it easier for them to create high-quality depth maps. |
Keywords
» Artificial intelligence » Cnn » Probability » Self supervised
