Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917829856460800 |
|---|---|
| author | Tian, Qingyao Liao, Huai Huang, Xinyan Li, Lujie Liu, Hongbin |
| author_facet | Tian, Qingyao Liao, Huai Huang, Xinyan Li, Lujie Liu, Hongbin |
| contents | Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synthetic data with depth labels for training and adapts domain knowledge for accurate depth estimation in real bronchoscope data. Our network demonstrates improved depth prediction on real footage using domain adaptation compared to training solely on synthetic data, validating our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation Tian, Qingyao Liao, Huai Huang, Xinyan Li, Lujie Liu, Hongbin Image and Video Processing Computer Vision and Pattern Recognition Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synthetic data with depth labels for training and adapts domain knowledge for accurate depth estimation in real bronchoscope data. Our network demonstrates improved depth prediction on real footage using domain adaptation compared to training solely on synthetic data, validating our approach. |
| title | Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.04404 |