首个真实胃镜视图合成数据集，解决内窥镜视觉扩展难题
Gastroendoscopy View Synthesis: A New Real Dataset and Evaluation
- 构建真实胃镜图像、相机位姿与点云组成的视图合成数据集
- 首次在真实胃镜场景中评估3DGS方法性能，揭示关键挑战
- 适合内窥镜视觉、医学影像与3D重建研究者使用
新颖的视图合成（NVS）是计算机视觉的活跃研究方向，得益于神经辐射场（NeRF）和3D高斯泼溅（3DGS）方法的成功。尽管NVS为胃镜检查带来新可能，如扩展视野、实现3D存档与医生训练的数字孪生，但现有数据集不足以评估其在胃镜中的应用。本文提出首个真实的胃镜视图合成数据集——GastroNVS，包含一组胃镜图像、相机位姿及真实胃镜检查的点云数据。为评估该数据集的适用性，我们测试了多种3DGS方法，并讨论未来发展的挑战。数据集可向项目主页申请获取。
原文摘要 · Abstract (English)
Novel view synthesis (NVS) is an active research topic in computer vision, owing to the success of neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) methods. While NVS opens the door to potential applications in gastroendoscopy, such as extending the field of view of endoscopic images and enabling digital twins for 3D archiving and endoscopist manipulation training, the dataset is insufficient to evaluate NVS for gastroendoscopy. In this paper, we present the first real gastroscopy dataset for NVS, namely the GastroNVS dataset, which contains a set of gastroscopic images, camera poses, and a point cloud for real gastroendoscopy inspection. To assess the suitability of the GastroNVS dataset, we evaluate several 3DGS methods and discuss the challenges for future development. The dataset is available on request from our project page.
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