Summary of Tapvid-3d: a Benchmark For Tracking Any Point in 3d, by Skanda Koppula et al.
TAPVid-3D: A Benchmark for Tracking Any Point in 3D
by Skanda Koppula, Ignacio Rocco, Yi Yang, Joe Heyward, João Carreira, Andrew Zisserman, Gabriel Brostow, Carl Doersch
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TAPVid-3D benchmark aims to evaluate the task of long-range tracking any point in 3D, a crucial step towards understanding precise 3D motion and surface deformation from monocular video. The authors build a new benchmark featuring over 4,000 real-world videos, covering various object types, motion patterns, and indoor/outdoor environments. They formulate a set of metrics to handle complexities like ambiguous depth scales, occlusions, and multi-track spatio-temporal smoothness. Competitive baselines are constructed using existing tracking models, serving as a guidepost for improving the TAP-3D task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new benchmark called TAPVid-3D that helps machines track points in 3D videos. This is important because it can help us understand how things move and change shape from just looking at one video. The researchers collected over 4,000 real-life videos with different objects, movements, and settings. They came up with special measures to compare different models’ performance on this task. |
Keywords
» Artificial intelligence » Tracking
