TAPIP3D: Novel Approach Improves 3D Point Tracking in Videos

3D Point Tracking Reaches New Dimensions with TAPIP3D

Tracking points in 3D videos is a challenge that plays a crucial role in many fields, from robotics to film production. A new approach called TAPIP3D promises significant improvements in this area. The method enables long-term tracking of 3D points in monocular RGB and RGB-D videos, relying on a novel method of camera stabilization and spatio-temporal feature clouds.

TAPIP3D transforms video data into stabilized 3D point clouds. Depth information and camera motion data are used to project 2D video features into a 3D world space where camera motion is effectively compensated. This stabilized 3D space forms the basis for the iterative refinement of motion estimations over multiple frames, enabling robust tracking over extended periods.

A particular challenge in 3D point tracking lies in the irregular distribution of 3D points. To address this issue, TAPIP3D employs a so-called "Local Pair Attention" mechanism. This 3D contextualization strategy leverages spatial relationships in 3D space to form informative feature neighborhoods, thus ensuring accurate estimation of 3D trajectories.

The results of TAPIP3D are promising. The 3D-centered approach significantly outperforms existing 3D point tracking methods. Interestingly, when accurate depth information is available, TAPIP3D even improves the accuracy of 2D tracking compared to conventional 2D pixel trackers. The system supports inference in both camera coordinates (unstabilized) and world coordinates. The results show that compensating for camera motion improves tracking performance.

In contrast to previous 2D and 3D trackers, which rely on conventional square correlation neighborhoods, TAPIP3D uses an innovative approach. This allows the method to achieve more robust and accurate results in various 3D point tracking benchmarks.

TAPIP3D represents a significant advance in the field of 3D point tracking. The combination of camera stabilization, spatio-temporal feature clouds, and the "Local Pair Attention" mechanism enables robust and accurate tracking of points over extended periods. This technology has the potential to revolutionize numerous applications in various fields, from autonomous navigation to virtual reality.

Bibliographie: http://arxiv.org/abs/2407.05921 https://arxiv.org/html/2407.05921v1 https://proceedings.neurips.cc/paper_files/paper/2024/file/9566607d423f8c32a2d5ce09a8b62232-Paper-Datasets_and_Benchmarks_Track.pdf https://paperswithcode.com/dataset/tapvid-3d-a-benchmark-for-tracking-any-point https://tapvid3d.github.io/ https://deepmind-tapir.github.io/