解决无纹理器械的精准6D位姿估计难题，仅用RGB图像实现高精度定位。
SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimation
- 基于密集对应关系设计专用框架，提升无纹理器械位姿估计稳定性。
- 在3个真实数据集上显著优于现有方法，实现亚像素级精度。
- 构建新数据集SynSurg6D，缓解标注数据稀缺问题，适合手术机器人研究者。
外科器械位姿估计对自主手术机器人、技能评估和手术流程标准化等应用至关重要。但由于精度要求高、频繁遮挡、器械无纹理、深度信息匮乏以及标注数据极少，传统通用位姿估计方法表现不佳。为此，我们构建了新数据集SynSurg6D以缓解数据短缺问题，并提出SurfSurg6D框架，专为外科器械位姿估计设计。在SurgRIPE、EndoVis2018和SurgPose数据集上的实验表明，引入合成数据集SynSurg6D可丰富姿态分布，提升现有方法性能；SurfSurg6D在仅使用RGB图像的情况下仍能实现鲁棒且精确的6D位姿估计，优于当前主流方法。
原文摘要 · Abstract (English)
Surgical instrument pose estimation provides crucial information for promising applications, including autonomous robotic surgery, skill assessment, and standardization of surgical workflow. However, this task remains highly challenging due to high precision requirements, frequent occlusions, textureless instruments, scarcity of depth information and very limited annotated data. These constraints often lead to unsatisfactory performance when employing general object pose estimation approaches to surgical scenarios. To address these issues, we first construct a new dataset SynSurg6D, to alleviate the data shortage in this task. We further propose SurfSurg6D, a dense-correspondence framework tailored for surgical instrument pose estimation. Experimental results on the SurgRIPE, EndoVis2018 and SurgPose datasets demonstrate that the introduction of our generated dataset SynSurg6D is able to diversify the pose distributions, thus enhancing the performance of existing approaches. Furthermore, SurfSurg6D outperforms existing methods, providing a robust solution for precise and efficient RGB-only pose estimation.
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