用合成数据训练，通过领域自适应提升支气管镜图像深度估计精度。
Enhancing Bronchoscopy Depth Estimation through Synthetic-to-Real Domain Adaptation
- 基于合成带标注数据训练，再通过领域自适应迁移到真实支气管镜图像。
- 在真实数据上深度预测误差显著低于仅用合成数据训练的模型。
- 适合需要低成本标注的医疗内窥镜三维重建研究者。
单目深度估计在通用图像任务中表现出色，有助于定位与三维重建。尽管如此，其在支气管镜图像中的应用受限于缺乏标注数据，难以采用监督学习方法。本文提出一种迁移学习框架，利用带深度标签的合成数据进行训练，并通过领域自适应将知识迁移到真实支气管镜图像中，实现更准确的深度估计。实验表明，相比仅使用合成数据训练的模型，本方法在真实视频上的深度预测性能显著提升，验证了该策略的有效性。
原文摘要 · Abstract (English)
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.
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