arXiv Open Access 2024

Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation

Qingyao Tian Huai Liao Xinyan Huang Lujie Li Hongbin Liu

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (5)

Qingyao Tian

Huai Liao

Xinyan Huang

Lujie Li

Hongbin Liu

Format Sitasi

APA MLA BibTeX

Tian, Q., Liao, H., Huang, X., Li, L., Liu, H. (2024). Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation. https://arxiv.org/abs/2411.04404

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓