无需模型或先验，用2D点追踪实现多物体6自由度姿态跟踪与重建
Point2Pose: Occlusion-Recovering 6D Pose Tracking and 3D Reconstruction for Multiple Unknown Objects Via 2D Point Trackers
- 仅靠物体表面稀疏点，通过2D点追踪实现长期关联
- 可完全遮挡后瞬间恢复姿态，支持多物体同时跟踪
- 适合无模型、复杂遮挡场景下的实时3D重建与定位
我们提出Point2Pose，一种从单目RGB-D视频中进行因果6自由度姿态跟踪的模型无关方法。仅需物体表面稀疏图像点初始化，即可在不依赖物体CAD模型或类别先验的情况下，跟踪多个未见刚体。该方法利用2D点追踪器获取长程对应关系，在完全遮挡后实现即时恢复。同时，系统增量式重建被跟踪目标的在线截断符号距离函数（TSDF）表示。此外，我们构建了一个包含仿真和真实序列的新多物体跟踪数据集，并提供动作捕捉真值用于评估。实验表明，Point2Pose在单物体精度上略有牺牲，但显著提升了模型无关跟踪能力，包括多物体跟踪和完全遮挡后的恢复能力。
原文摘要 · Abstract (English)
We present Point2Pose, a model-free method for causal 6D pose tracking of multiple rigid objects from monocular RGB-D video. Initialized only from sparse image points on the objects, our approach tracks multiple unseen objects without requiring object CAD models or category priors. Point2Pose leverages a 2D point tracker to obtain long-range correspondences, enabling instant recovery after complete occlusion. Simultaneously, the system incrementally reconstructs an online Truncated Signed Distance Function (TSDF) representation of the tracked targets. Alongside the method, we introduce a new multi-object tracking dataset comprising both simulation and real-world sequences, with motion-capture ground truth for evaluation. Experiments show that Point2Pose trades some single-object pose accuracy for broader model-free tracking capabilities, including multi-object tracking and recovery from complete occlusion. Project page: https://point2pose.github.io/.
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