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The 3D Tracker Repairs the Lifted Trajectory
ReLift treats dense monocular 3D tracking as geometric correction and completion instead of relearning correspondence, depth and occlusion together.
Summary
ReLift treats dense monocular 3D tracking as geometric correction and completion instead of relearning correspondence, depth and occlusion together.
Strong pretrained 2D trackers and geometry models can already lift video points into 3D, but their trajectories inherit correspondence mistakes, depth errors and occluder depth during gaps. ReLift focuses 4D supervision on repairing those trajectories. Its feed-forward corrector alternates attention along and across tracks, preserves reliable observations, fixes corrupted positions and completes missing motion. Because it trains on geometric trajectories with simulated lifting errors, it can work across upstream trackers and geometry providers without retraining. On WorldTrack, the paper reports state-of-the-art mean accuracy, improving the strongest baselines by 2.1 APD overall and 3.0 on dynamic points. Those gains depend on the reported benchmark and input components.
Why it matters
ReLift treats dense monocular 3D tracking as geometric correction and completion instead of relearning correspondence, depth and occlusion together.
Limits and context
No additional limitation was separately recorded.
Key claims
ReLift treats dense monocular 3D tracking as geometric correction and completion instead of relearning correspondence, depth and occlusion together.
Evidence: source-2026-11-02-009
Sources
- arXiv preprint 2610.42136, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.