RealSynCol：一种高保真的合成结肠数据集用于3D重建应用
RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications
AI总结：
RealSynCol通过高保真的合成数据集提升3D重建应用中的深度和姿态估计性能，为内窥镜诊断提供有力支持。
摘要：
深度学习有潜力通过使结肠的3D重建成为可能，从而改进结肠镜检查。它能够提供粘膜表面和病变的全面视图，并促进未探索区域的识别。然而，开发稳健的方法受到大规模真实数据稀缺的限制。我们提出了RealSynCol，一种高度逼真的合成数据集，旨在复制内窥镜环境。从10个CT扫描中提取的结肠几何体被导入一个近似手术条件的虚拟环境，并用逼真的血管纹理渲染。所得到的数据集包含28,130帧，配以真实深度图、光学流、3D网格和摄像机轨迹。进行了一项基准研究，以评估现有合成结肠数据集在深度和姿态估计任务中的表现。结果表明，RealSynCol的高真实性和多样性显著增强了在临床图像上的泛化性能，证明其是开发深度学习算法以支持内窥镜诊断的强大工具。
英文摘要：
Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces and lesions, and facilitating the identification of unexplored areas. However, the development of robust methods is limited by the scarcity of large-scale ground truth data. We propose RealSynCol, a highly realistic synthetic dataset designed to replicate the endoscopic environment. Colon geometries extracted from 10 CT scans were imported into a virtual environment that closely mimics intraoperative conditions and rendered with realistic vascular textures. The resulting dataset comprises 28\,130 frames, paired with ground truth depth maps, optical flow, 3D meshes, and camera trajectories. A benchmark study was conducted to evaluate the available synthetic colon datasets for the tasks of depth and pose estimation. Results demonstrate that the high realism and variability of RealSynCol significantly enhance generalization performance on clinical images, proving it to be a powerful tool for developing deep learning algorithms to support endoscopic diagnosis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
