Monocular 3D Object Localization

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (6 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.

• Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer
  28 Apr 2025 1 repository listed
  Due to the choice of our input data representation, physically correct synthetic training data, and using targeted augmentations, the network naturally generalizes to real data.
• Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
  26 Oct 2023 1 repository listed
  We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels.
• MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
  29 Jun 2020 1 repository listed
  Monocular multi-object detection and localization in 3D space has been proven to be a challenging task.
• MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
  26 Nov 2018 1 repository listed Syntology ran 4 of 5 samples · 1 unverified
  We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension.

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.