Abstract
Three-dimensional (3D) scene understanding in colonoscopy presents significant challenges that necessitate automated methods for accurate depth estimation. However, existing depth estimation models for endoscopy struggle with temporal consistency across video sequences, limiting their applicability for 3D reconstruction. We present ColonCrafter, a diffusion-based depth estimation model that generates temporally consistent depth maps from monocular colonoscopy videos. Our approach learns robust geometric priors from synthetic colonoscopy sequences to generate temporally consistent depth maps. We also introduce a style transfer technique that preserves geometric structure while adapting real clinical videos to match our synthetic training domain. ColonCrafter achieves state-of-the-art zero-shot performance on the C3VD dataset, outperforming both general-purpose and endoscopy-specific approaches. Although full trajectory 3D reconstruction remains a challenge, we demonstrate clinically relevant applications of ColonCrafter, including 3D point cloud generation and surface coverage assessment.
Abstract (translated)
在结肠镜检查中的三维（3D）场景理解面临着重大挑战，需要自动化的方法来准确估计深度。然而，现有的用于内窥镜的深度估计模型在视频序列的时间一致性方面存在问题，限制了它们在3D重建中的应用效果。我们提出了ColonCrafter，这是一种基于扩散过程的深度估计模型，可以从单目结肠镜检查视频中生成时间一致性的深度图。我们的方法从合成的结肠镜序列中学习到鲁棒的几何先验，以生成时间一致性好的深度地图。此外，我们还引入了一种风格迁移技术，可以在保留几何结构的同时将实际临床视频适配到我们的合成训练领域。 ColonCrafter在C3VD数据集上实现了最先进的零样本性能，优于通用模型和专门针对内窥镜的方法。尽管完整的轨迹3D重建仍然是一个挑战，但我们展示了ColonCrafter的一些具有临床意义的应用，包括生成3D点云和评估表面覆盖情况。
URL
https://arxiv.org/abs/2509.13525
