多径盲点：用于基于冻结基础的稀疏锚点度量深度的K无关鲁棒校准
The Multipath Blind Spot: $K$-Agnostic Robust Calibration for Sparse-Anchor Metric Depth from Frozen Foundations
AI总结：
研究单目深度基础缺乏绝对尺度问题，提出多径鲁棒锚定校准方法，通过基础一致性筛选锚点，无需学习参数，在多种异常情况下效果优于现有方法。
摘要：
单目深度基础预测通用相对深度但缺乏绝对尺度，少量稀疏度量锚点可校准为度量深度。现有方法假设锚点无异常，实际传感器会产生异常值。本文指出现有校准方法在异常值下失效，提出多径鲁棒锚定校准（MRAC），通过基础一致性筛选锚点，在多种数据集上效果显著提升。
英文摘要：
Monocular depth foundations predict domain-general relative depth but lack absolute scale; a handful of sparse metric anchors from a range sensor can calibrate them to metric depth, an attractive alternative to metric-supervised training. Existing sparse-anchor calibration methods, however, assume the anchors are clean, whereas real sensors produce outliers that are present with the wrong value -- time-of-flight multipath, mixed pixels -- not merely missing. We show that the established residual-on-CFA calibration recipe collapses under such outliers, and that the strongest publicly deployed method, VI-Depth, has a structural multipath blind spot: robust to missing anchors, it falls behind an unprotected baseline on three of four datasets when anchors are present but wrong. We propose Multipath-Robust Anchor Calibration (MRAC), a parameter-free, inference-time wrapper that gates anchors by foundation consistency -- a Theil--Sen fit and a median-absolute-deviation test against the foundation's own relative-depth ordering -- before a single call to the calibration head. MRAC adds no learned parameters, runs its selection in $\approx 50\,μ$s on CPU, and serves anchor budgets $K \in [5,200]$ from one checkpoint. On a $320$-cell benchmark with a same-backbone, same-architecture control, MRAC strictly wins $84\%$ of same-backbone cells across all four outlier families and, against VI-Depth, wins all twelve corrupted multipath cells and all sixteen KITTI cells, reducing KITTI multipath AbsRel by $3.2\times$ ($0.489$ to $0.151$) at zero retraining.
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。
