改进内窥镜视频的3D重建，让特征点更准更密。
SuperPoint-E: local features for 3D reconstruction via tracking adaptation in endoscopy
- 用追踪适应监督策略优化特征提取。
- 特征点密度更高，匹配成功率提升，重建更完整。
- 适合需要高精度3D重建的医疗影像场景。
本文聚焦于提升内窥镜视频中结构光恢复（SfM）的特征提取性能。提出SuperPoint-E这一新型局部特征提取方法，通过所提出的追踪适应监督策略，显著提升了内窥镜场景下特征检测与描述的质量。在真实内窥镜视频上的大量实验验证了该方法的最佳配置，并评估了SuperPoint-E的特征质量。与其他基线方法对比表明，本方法生成的3D重建更密集，覆盖更多且更长的视频片段，因检测器触发更密集且特征更易存活（即检测精度更高）。此外，描述子更具区分性，使引导匹配步骤几乎冗余。相比原始SuperPoint及主流SfM工具COLMAP，本方法在内窥镜视频的3D重建上带来显著提升。
原文摘要 · Abstract (English)
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.
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