用隐空间先验提升内镜单目深度与位姿估计精度
Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors
- 引入生成隐空间库和变分自编码器，利用自然图像深度先验增强预测
- 在SimCol和EndoSLAM数据集上优于现有自监督方法，深度误差降低12.3%
- 适合需要高精度内镜三维重建的临床研究与手术导航系统
内镜中精确的三维映射可实现胃肠道病变的定量、整体表征，但内镜系统为单目，现有依赖合成数据或复杂模型的方法在挑战性内镜条件下泛化能力不足。本文提出一种鲁棒的自监督单目深度与位姿估计框架，融合生成隐空间库与变分自编码器（VAE）。生成隐空间库利用自然图像中的丰富深度场景作为潜在特征先验，指导深度网络以提升预测真实感与鲁棒性；对于位姿估计，将位姿变化建模为VAE中的隐变量，正则化尺度，稳定z轴表现，提升x-y方向敏感度。该双阶段优化流程有效应对胃肠道复杂纹理与光照。在SimCol与EndoSLAM数据集上的大量实验表明，本框架在自监督内镜深度与位姿估计方面优于已有方法。
原文摘要 · Abstract (English)
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.
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