Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reliable depth and pose estimation. However, endoscopy systems are monocular, and existing methods relying on synthetic datasets or complex models often lack generalizability in challenging endoscopic conditions. We propose a robust self-supervised monocular depth and pose estimation framework that incorporates a Generative Latent Bank and a Variational Autoencoder (VAE). The Generative Latent Bank leverages extensive depth scenes from natural images to condition the depth network, enhancing realism and robustness of depth predictions through latent feature priors. For pose estimation, we reformulate it within a VAE framework, treating pose transitions as latent variables to regularize scale, stabilize z-axis prominence, and improve x-y sensitivity. This dual refinement pipeline enables accurate depth and pose predictions, effectively addressing the GI tract's complex textures and lighting. Extensive evaluations on SimCol and EndoSLAM datasets confirm our framework's superior performance over published self-supervised methods in endoscopic depth and pose estimation.

本研究解决了内窥镜中的深度与姿态估计问题，尤其在复杂的胃肠道条件下现有方法的局限性。通过引入生成潜在库和变分自编码器，提出了一种稳健的自监督框架，有效提升了深度预测的真实感与准确性。实验结果表明，该方法在深度与姿态估计方面显著优于现有自监督技术，具有重要的临床应用潜力。

基于生成潜在先验的自监督单目深度与姿态估计在内窥镜中的应用