GAUGE：用于前馈4D跟踪的组别视图不一致性修正
GAUGE: Group-Wise View-Inconsistency Rectification for Feed-Forward 4D Tracking
AI总结：
针对前馈4D跟踪中全局对齐后残差缺乏结构解释的问题，提出无需训练且模型无关的后处理模块GAUGE，利用方向一致性和空间连通性恢复运动组，并通过度量锚点估计径向尺度与组别平移，显著降低端点误差。
AI中文摘要：
前馈模型直接从单目视频回归稠密3D点轨迹，然而全局对齐后的残差仍然显著且缺乏结构性的解释。在跨模型和数据集上的动态查询点上测量发现，误差沿视图方向集中，而每个运动组所需的尺度修正各不相同。尽管如此，预测的位移方向支持可靠的组别划分，其中位角远低于90°的随机基线。因此，残差的系统性部分表现为每个运动组沿2D观测无法约束的方向的一系列径向自由度，我们称之为组别视图不一致性。我们提出GAUGE（组别自适应无监督尺度估计），一个无需训练且与模型无关的后处理模块。它从方向一致性和空间连通性中恢复运动组，然后利用1%至5%的度量锚点估计每帧的径向尺度和组别平移，每组每帧具有四个自由度。在包括D4RT、4RC和SM4RT在内的八个跟踪器的动态查询点上，我们的修正将端点误差相对于未修正的预测降低了15.1%至62.6%，而将相同的锚点用于梯度微调仅使同一模型提升-1.1%至15.2%。代码公开于该https URL。
英文摘要：
Feed-forward models regress dense 3D point trajectories directly from monocular video, yet the residual after global alignment is substantial and lacks a structural explanation. Measured on dynamic query points across models and datasets, the error concentrates along the view direction, while the scale correction each motion group requires differs. The predicted displacement direction nevertheless supports reliable grouping, with a median angle far below the 90° random baseline. The systematic part of the residual is therefore a family of radial degrees of freedom per motion group, along directions 2D observations cannot constrain. We call it group-wise view inconsistency. We present GAUGE (Group-wise Adaptive Unsupervised Gauge Estimation), a training-free and model-agnostic post-hoc module. It recovers motion groups from direction consistency and spatial connectivity, then estimates a per-frame radial scale and group-level translation from 1% to 5% metric anchors, four degrees of freedom per group and frame. On dynamic query points of eight trackers, including D4RT, 4RC and SM4RT, our correction lowers endpoint error by 15.1% to 62.6% over the uncorrected predictions, while spending the same anchors on gradient fine-tuning improves the same models by only -1.1% to 15.2%. Code is publicly available at https://github.com/HCPLab-SYSU/GAUGE.
