SuperPoint-E：通过内窥镜跟踪适应进行3D重建的局部特征
SuperPoint-E: local features for 3D reconstruction via tracking adaptation in endoscopy
AI总结：
SuperPoint-E通过跟踪适应策略提升内窥镜视频中3D重建的特征提取性能，实现更密集和更精确的重建结果。
摘要：
在本工作中，我们关注通过提升特征提取来改进内窥镜视频中结构从运动（SfM）的性能。我们提出了SuperPoint-E，一种新的局部特征提取方法，利用我们提出的目标跟踪适应监督策略，显著提高了内窥镜中特征检测和描述的质量。在真实内窥镜记录上进行了广泛的实验，研究了我们方法最合适的配置，并评估了SuperPoint-E特征的质量。与其他基线的比较也显示，我们的3D重建更密集，覆盖更多且更长的视频片段，因为我们的检测器更密集地触发，我们的特征更有可能存活（即更高的检测精度）。此外，我们的描述符更具判别性，使引导匹配步骤几乎变得多余。所提出的方法在通过内窥镜视频进行SfM获得的3D重建中，相比原始SuperPoint和黄金标准SfM COLMAP流程有显著改进。
英文摘要：
In this work, we focus on boosting the feature extraction to improve the performance of Structure-from-Motion (SfM) in endoscopy videos. We present SuperPoint-E, a new local feature extraction method that, using our proposed Tracking Adaptation supervision strategy, significantly improves the quality of feature detection and description in endoscopy. Extensive experimentation on real endoscopy recordings studies our approach's most suitable configuration and evaluates SuperPoint-E feature quality. The comparison with other baselines also shows that our 3D reconstructions are denser and cover more and longer video segments because our detector fires more densely and our features are more likely to survive (i.e. higher detection precision). In addition, our descriptor is more discriminative, making the guided matching step almost redundant. The presented approach brings significant improvements in the 3D reconstructions obtained, via SfM on endoscopy videos, compared to the original SuperPoint and the gold standard SfM COLMAP pipeline.
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。
