SOURCE-LINKED INTELLIGENCE
TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
Multi-camera systems are increasingly practical for robotics, AR/VR, and autonomous driving because complementary views reduce depth ambiguity and preserve visibility under occlusion. Existing point-tracking benchmarks, however, focus on a single video or static multi-camera rigs. None test long-term 3D point tracking across several synchronized views under camera motion. We introduce TAPVid-MV (Tracking Any Point in Video across Multiple Views), the first benchmark for this setting. It contains a curated set of 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks across se
Read original source ↗ Open in workspace
- recordType
- paper
- region
- Global
Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-01T21:58:51.000Z
First collected: 2026-09-21T05:51:54.566Z. This is not the publication date.