用分割轮廓估计平面物体的精准位姿，性能大幅领先。
Segmentation-Guided Homography Estimation for Long-Term Planar Tracking
- 从分割掩码轮廓提取几何信息，无须训练即可估算8自由度单应性。
- 在PlanarTrack上比之前方法高出18.4个百分点，刷新纪录。
- 适合需要高精度位姿估计的视觉跟踪任务，如机器人导航。
近期先进的视觉跟踪器可生成高质量且长期稳定的分割掩码。本文提出利用这些优势来实现平面物体跟踪，目标是估计精确的8自由度单应性位姿（一种分割追踪器不输出的几何表示）。我们提出SAM-H——一种基于分割掩码轮廓、无需训练的平面物体跟踪器。当应用于SAM 2生成的掩码时，SAM-H在具有挑战性的PlanarTrack基准上以显著优势达到新状态，p@5指标提升18.4个百分点。我们进一步证明，基于分割与基于对应关系的单应性估计方法具有互补性，并提出WOFTSAM，其在PlanarTrack和POT-210两个基准上均优于所有先前方法。此外，我们还提供了对PlanarTrack初始位姿的精确重标注，使高精度的p@5评估更准确。代码与重标注数据已公开于https://github.com/serycjon/WOFTSAM。
原文摘要 · Abstract (English)
Recent state-of-the-art visual trackers produce high quality and long-term-stable segmentation masks. We propose to leverage these strengths for planar object tracking, in which the goal is to estimate a precise 8-degrees-of-freedom homography pose, a geometric representation not estimated by segmentation trackers. We present SAM-H - a planar object tracker that estimates homographies from segmentation mask contours via a training-free pipeline. When SAM-H is applied to masks from SAM 2, it sets a new state-of-the-art performance on the challenging PlanarTrack benchmark by a large margin, +18.4pp on the p@5 metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFTSAM, which out-performs all prior methods on both PlanarTrack and POT-210. We also provide precise re-annotations of PlanarTrack initial poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available at https://github.com/serycjon/WOFTSAM
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