Daily AI Paper

Daily AI Paper

2026-09-25

Archived
2026-09-25

TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations

Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta, Fan Zhang, Tanner Schmidt, Jakob Engel, et al.

arXiv:2609.30222v1

Today’s standout is TrackEverything, a new way to track points through very long videos. The core problem is a familiar tradeoff: existing trackers can follow only a few points for a long time, or many points for a short time, but not both. TrackEverything breaks that limit by treating the video as a sequence of 3D scene tracks in world coordinates, rather than as isolated 2D frames. That lets the model scale with the actual geometry of the scene, not with video length. It also deduplicates repeated observations in overlapping windows, so the same surface does not get tracked over and over again. Then it separates static from dynamic points, using a lightweight refinement step only where motion actually happens. This matters because long-horizon dense tracking is foundational for robotics, AR, video understanding, and scene reconstruction. The result is a tracker that can handle videos over 1,000 frames while staying within practical GPU memory, and still tracks far more points than prior long-video systems.

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