提升内镜单目SLAM的3D地图密度与精度，适合临床应用。
3D Densification for Multi-Map Monocular VSLAM in Endoscopy
- 用LMedS对齐稀疏特征图与深度预测，去除异常点。
- 在C3VD数据集上实现4.15毫米的均方根误差，计算效率高。
- 适用于需要高精度3D重建的内镜手术导航场景。
单目内镜序列的多地图稀疏视觉定位与建图已在频繁丢失跟踪（如运动模糊、遮挡、器械干扰或水喷射）时展现出鲁棒性。然而，稀疏多地图在环境表征上表现差，存在大量噪声点、错误重建点及显著离群点，且密度极低，难以满足临床需求。本文提出一种方法，在现有稀疏端镜多地图CudaSIFT-SLAM基础上，通过基于鲁棒性的LMedS对齐无尺度深度预测网络NN LightDepth与稀疏子地图，实现去噪与稠密化。该系统缓解了单目深度估计的固有尺度模糊性，同时过滤离群点，生成可靠稠密3D地图。在C3VD模拟结肠数据集上，验证了4.15毫米均方根精度，且计算开销可接受；并在真实结肠镜数据集Endomapper上展示定性结果。
原文摘要 · Abstract (English)
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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