Papers › Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Matthias Grundmann
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14,819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at http://www.objectron.dev
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Monocular 3D Object Detection | Google Objectron | EfficientNetLite + keypoint regressor | AP at 10' Elevation error | 0.8584 | #2 of 3 | Archive leaderboard | report |
| Monocular 3D Object Detection | Google Objectron | EfficientNetLite + keypoint regressor | AP at 15' Azimuth error | 0.7844 | #2 of 3 | Archive leaderboard | report |
| Monocular 3D Object Detection | Google Objectron | EfficientNetLite + keypoint regressor | Average Precision at 0.5 3D IoU | 0.6512 | #2 of 3 | Archive leaderboard | report |
| Monocular 3D Object Detection | Google Objectron | EfficientNetLite + keypoint regressor | MPE | 0.0467 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections