论文标题
双峰摄像头姿势预测内窥镜检查
Bimodal Camera Pose Prediction for Endoscopy
论文作者
论文摘要
从图像中推论内窥镜场景的3D结构极具挑战性。除了变形和依赖视图的照明外，诸如结肠之类的管状结构源于其自我封闭和重复的解剖结构所引起的问题。在本文中，我们提出了SIMCOL，这是一个用于结肠镜检查中相机姿势估计的合成数据集，以及一种明确学习双峰分布以预测内窥镜姿势的新方法。我们的数据集复制了真正的结肠镜运动，并突出了现有方法的缺点。我们从模拟的结肠镜检查中发布了18K RGB图像，并具有相应的深度和相机姿势，并公开提供了Unity的数据生成环境。我们评估了不同的摄像头姿势预测方法，并证明，在对数据进行训练时，它们将其推广到实际的结肠镜序列，而双峰方法的表现优于事先的单峰工作。
Deducing the 3D structure of endoscopic scenes from images is exceedingly challenging. In addition to deformation and view-dependent lighting, tubular structures like the colon present problems stemming from their self-occluding and repetitive anatomical structure. In this paper, we propose SimCol, a synthetic dataset for camera pose estimation in colonoscopy, and a novel method that explicitly learns a bimodal distribution to predict the endoscope pose. Our dataset replicates real colonoscope motion and highlights the drawbacks of existing methods. We publish 18k RGB images from simulated colonoscopy with corresponding depth and camera poses and make our data generation environment in Unity publicly available. We evaluate different camera pose prediction methods and demonstrate that, when trained on our data, they generalize to real colonoscopy sequences, and our bimodal approach outperforms prior unimodal work.
