Adversarial Diffusion Model for Domain-Adaptive Depth Estimation in Bronchoscopic Navigation

In bronchoscopic navigation, depth estimation has emerged as a promising method with higher robustness for localizing camera and obtaining scene geometry. While many supervised approaches have shown success for natural images, the scarcity of depth annotations limits their deployment in bronchoscopic scenarios. To address the issue of lacking depth labels, a common approach for unsupervised domain adaptation (UDA) includes one-shot mapping through generative adversarial networks. However, conventional adversarial models that directly recover the image distribution can suffer from reduced sample fidelity and learning biases. In this study, we propose a novel adversarial diffusion model for domain-adaptive depth estimation on bronchoscopic images. Our two-stage approach sequentially trains a supervised network on labeled virtual images, and an unsupervised adversarial network that aligns domain-invariant representations for cross-domain adaptation. This model reformulates depth estimation at each stage as an iterative diffusion-denoising process within the latent space for mitigating mapping biases and enhancing model performance. The experiments on clinical sequences show the superiority of our method on depth estimation as well as geometry reconstruction for bronchoscopic navigation.