Summary of Self-supervised Monocular Depth and Pose Estimation For Endoscopy with Generative Latent Priors, by Ziang Xu et al.
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Generative Latent Priors
by Ziang Xu, Bin Li, Yang Hu, Chenyu Zhang, James East, Sharib Ali, Jens Rittscher
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework combines a Generative Latent Bank and Variational Autoencoder (VAE) for robust self-supervised monocular depth and pose estimation in endoscopy. The Generative Latent Bank leverages extensive natural image data to enhance the realism and robustness of depth predictions, while the VAE regularizes pose transitions to stabilize scale, z-axis prominence, and improve x-y sensitivity. This dual refinement pipeline achieves accurate depth and pose predictions even in challenging endoscopic conditions with complex textures and lighting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Endoscopy uses a camera to look inside your body, which helps doctors check for problems like ulcers or tumors. To make this process better, researchers are working on new ways to use the images taken by the camera. One important step is to figure out how far away things are (called depth) and where the camera is pointing (called pose). Most methods rely on fake data or complicated models, but these don’t work well in real-world situations. The new framework uses a special combination of two techniques called Generative Latent Bank and Variational Autoencoder to make depth and pose predictions more accurate. |
Keywords
» Artificial intelligence » Pose estimation » Self supervised » Variational autoencoder
