3D致密化用于内窥镜多地图单目视觉SLAM
3D Densification for Multi-Map Monocular VSLAM in Endoscopy
AI总结：
本文提出了一种方法，通过去除异常值和增强地图密度，改进了内窥镜多地图单目视觉SLAM中的3D环境表示，实现了在临床应用中更精确的3D地图重建。
摘要：
多地图稀疏单目视觉同时定位与建图应用于单目内窥镜序列已被证明在内窥镜中频繁的损失（如运动模糊、时间遮挡、工具交互或水喷射）后能够稳健地恢复跟踪。稀疏多地图对于稳健的相机定位是足够的，但它们在环境表示方面非常差，它们是嘈杂的，有高比例的不准确重建的3D点，包括显著的异常值，更重要的是在临床应用中具有不可接受的低密度。我们提出了一种方法来去除异常值并增强状态-of-the-art稀疏内窥镜多地图CudaSIFT-SLAM的地图。通过使用鲁棒的LMedS将NN LightDepth用于到尺度的深度密集预测对齐稀疏CudaSIFT子地图。我们的系统缓解了单目深度估计中的固有尺度模糊问题，同时过滤异常值，导致可靠的致密3D地图。我们在C3VD幻影结肠数据集中提供了准确致密地图的实验证据，4.15毫米RMS精度在可接受的计算时间内。我们还报告了在Endomapper数据集上的真实结肠镜的定性结果。
英文摘要：
Multi-map Sparse Monocular visual Simultaneous Localization and Mapping applied to monocular endoscopic sequences has proven efficient to robustly recover tracking after the frequent losses in endoscopy due to motion blur, temporal occlusion, tools interaction or water jets. The sparse multi-maps are adequate for robust camera localization, however they are very poor for environment representation, they are noisy, with a high percentage of inaccurately reconstructed 3D points, including significant outliers, and more importantly with an unacceptable low density for clinical applications. We propose a method to remove outliers and densify the maps of the state of the art for sparse endoscopy multi-map CudaSIFT-SLAM. The NN LightDepth for up-to-scale depth dense predictions are aligned with the sparse CudaSIFT submaps by means of the robust to spurious LMedS. Our system mitigates the inherent scale ambiguity in monocular depth estimation while filtering outliers, leading to reliable densified 3D maps. We provide experimental evidence of accurate densified maps 4.15 mm RMS accuracy at affordable computing time in the C3VD phantom colon dataset. We report qualitative results on the real colonoscopy from the Endomapper dataset.
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