Syn2RealTrack：弥合合成数据集与真实世界数据集之间的差距，用于在线多视图多目标跟踪
Syn2RealTrack: Bridging the Gap Between Synthetic and Real-World Datasets for Online Multi-View Multi-Target Tracking
浏览论文内容
中文总结 AI 辅助
Syn2RealTrack将合成到真实域差距分解为三个环节并对应局部解决方案，无需重训特征提取器，在AI City Challenge 2026 Track 1评估中3D HOTA达52.0118%，用于在线多视图多目标跟踪
中文摘要 AI 辅助
仓库场景的多相机3D感知系统大多在合成数据上训练，并在物理采集的环境中进行评估。由此产生的合成到真实域差距会破坏地面平面定位和跨相机身份关联，通常被视为单个域适应模块即可解决的缺陷；我们认为该差距会在三个可分离的环节进入流程：相机标定、物体形状先验以及物体数量已知的假设，每个环节都有不同的局部解决方案。我们的在线流程Syn2RealTrack遵循这种分解：在无标定的情况下仅从图像中恢复镜头畸变；通过可见性加权的基于部件的描述符融合跨视图检测，该描述符对遮挡部件弃权（不执行）而非猜测；从标定中以闭式形式测量人体高度，而非从合成先验中复制；将封闭世界数量先验与因果滤波器配对，以消除该先验生成的幻影框。因此，该系统通过重新分配几何与外观之间的信任进行适应，无需重新训练特征提取器。在AI City Challenge 2026 Track 1评估服务器上，其3D高阶跟踪准确率（HOTA）达到52.0118%。代码将在该httpsURL发布
英文摘要
Multi-camera 3D perception systems for warehouse scenes are trained largely on synthetic data and evaluated on physically captured environments. The resulting synthetic-to-real gap, which corrupts ground-plane localization and cross-camera identity association, is usually treated as one deficiency for a single domain-adaptation module to absorb; we argue instead that it enters the pipeline at three separable points: the camera calibration, the object shape prior, and the assumption that the object census is known, each admitting a different local remedy. Our online pipeline, Syn2RealTrack, follows this decomposition: lens distortion is recovered from images alone under a calibration that provides none, detections are fused across views by a visibility-weighted part-based descriptor that abstains on occluded parts rather than guessing, person height is measured in closed form from calibration instead of copied from a synthetic prior, and a closed-world cardinality prior is paired with a causal filter that removes the phantom boxes the prior manufactures. The system therefore adapts by reallocating trust between geometry and appearance without retraining a feature extractor. On the AI City Challenge 2026 Track~1 evaluation server it reaches a 3D Higher Order Tracking Accuracy (HOTA) of 52.0118%. The code will be released at https://github.com/SKKUAutoLab/aic26_mc3dp
发表机构
- Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。
