3D Pedestrian Tracking

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (4 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Center-based 3D Object Detection and Tracking
  19 Jun 2020 13 repositories listed Syntology ran 7 of 22 samples · 15 unverified
  Three-dimensional objects are commonly represented as 3D boxes in a point-cloud.
• Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
  21 May 2025 1 repository listed
  To tackle the challenges, we first collect a new multi-view LiDAR-camera 3D multiple-object-tracking benchmark of highly crowded pedestrians for in-depth analysis.
• Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving
  26 Dec 2020 1 repository listed
  Second, we propose to learn a metric that combines the Mahalanobis and feature distances when comparing a track and a new detection in data association.
• 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
  9 Jul 2019 1 repository listed
  Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.