TAPIP3D: Tracking Any Point in Persistent 3D Geometry
We introduce TAPIP3D, a novel approach for long-term 3D point tracking in
monocular RGB and RGB-D videos. TAPIP3D represents videos as camera-stabilized
spatio-temporal feature clouds, leveraging depth and camera motion information
to lift 2D video features into a 3D world space where camera motion is
effectively canceled. TAPIP3D iteratively refines multi-frame 3D motion
estimates within this stabilized representation, enabling robust tracking over
extended periods. To manage the inherent irregularities of 3D point
distributions, we propose a Local Pair Attention mechanism. This 3D
contextualization strategy effectively exploits spatial relationships in 3D,
forming informative feature neighborhoods for precise 3D trajectory estimation.
Our 3D-centric approach significantly outperforms existing 3D point tracking
methods and even enhances 2D tracking accuracy compared to conventional 2D
pixel trackers when accurate depth is available. It supports inference in both
camera coordinates (i.e., unstabilized) and world coordinates, and our results
demonstrate that compensating for camera motion improves tracking performance.
Our approach replaces the conventional 2D square correlation neighborhoods used
in prior 2D and 3D trackers, leading to more robust and accurate results across
various 3D point tracking benchmarks. Project Page: https://tapip3d.github.io
680720e066b60d551b653f1b
Bowei Zhang
680720e066b60d551b653f1c
Lei Ke
admin_assigned
2025-04-22T11:05:00.815Z
6258a6455ea3a0a9b6de3f22
/avatars/6eeed72a97fb24465e5e65583fbe50cf.svg
Lei Ke
user
lkeab
680720e066b60d551b653f1d
Adam W. Harley
admin_assigned
2025-04-22T11:04:46.261Z
6314ff478be84e1ab412155a
/avatars/5a0c0aedc99aac6731e0652faf98b2e6.svg
Adam W Harley
user
aharley
680720e066b60d551b653f1e
Katerina Fragkiadaki
TAPIP3D: Tracking Any Point in Persistent 3D Geometry
We introduce TAPIP3D, a novel approach for long-term 3D point tracking in
monocular RGB and RGB-D videos. TAPIP3D represents videos as camera-stabilized
spatio-temporal feature clouds, leveraging depth and camera motion information
to lift 2D video features into a 3D world space where camera motion is
effectively canceled. TAPIP3D iteratively refines multi-frame 3D motion
estimates within this stabilized representation, enabling robust tracking over
extended periods. To manage the inherent irregularities of 3D point
distributions, we propose a Local Pair Attention mechanism. This 3D
contextualization strategy effectively exploits spatial relationships in 3D,
forming informative feature neighborhoods for precise 3D trajectory estimation.
Our 3D-centric approach significantly outperforms existing 3D point tracking
methods and even enhances 2D tracking accuracy compared to conventional 2D
pixel trackers when accurate depth is available. It supports inference in both
camera coordinates (i.e., unstabilized) and world coordinates, and our results
demonstrate that compensating for camera motion improves tracking performance.
Our approach replaces the conventional 2D square correlation neighborhoods used
in prior 2D and 3D trackers, leading to more robust and accurate results across
various 3D point tracking benchmarks. Project Page: https://tapip3d.github.io
A novel 3D point tracking method called TAPIP3D stabilizes video features into a 3D space using depth and motion data, refining 3D trajectories and surpassing 2D and conventional 3D tracking methods.
camera-stabilized spatio-temporal feature clouds depth information 3D world space camera motion multi-frame 3D motion estimates Local Pair Attention 3D contextualization 3D trajectory estimation 3D-centric approach 2D pixel trackers correlation neighborhoods