ReLift: Dense 3D Tracking as Trajectory Repair

Pretrained 2D trackers and geometry models already provide strong estimates for monocular 3D tracking where their observations are reliable. Lifting 2D tracks with estimated depth and cameras yields trajectories that are accurate in most places but jitter along the viewing ray, jump to other surfaces near occlusions, and go missing while points are hidden. We recast dense 3D tracking as repairing this lift. A feed-forward corrector reads only the geometry of the lifted trajectories and predicts their corrections. Since it never sees an image, we train it only on clean PointOdyssey trajectories with simulated lifting errors, and one trained model serves every tracker and geometry source we evaluate. On WorldTrack, it achieves state-of-the-art mean accuracy, and with VGGT-$\Omega$ geometry it is competitive with the strongest 3D trackers at a fraction of their runtime.