{"url":"/dataset/aria-synthetic-environments","name":"Aria Synthetic Environments","full_name":"Aria Synthetic Environments","description_markdown":"[1]: https://www.projectaria.com/datasets/ase/ \"\"\r\n[2]: https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset \"\"\r\n[3]: https://www.projectaria.com/research/ \"\"\r\n\r\n**Aria Synthetic Environments** is a large-scale, fully simulated dataset created by Project Aria¹[1]. It consists of procedurally-generated interior layouts filled with 3D objects, simulated with the sensor characteristics of Aria glasses¹[1]. Here are some key features of this dataset:\r\n\r\n- **100,000 unique multi-room interior scenes**: These scenes are procedurally generated to produce a diverse set of interior scenes. Each scene has a unique room graph connecting multiple rooms, and unique placement of architectural features, such as windows, doors, and pillars¹[1].\r\n- **Populated with high-quality 3D objects**: Each of the 100,000 unique scenes is filled with objects from a digital library, each with high-quality materials and geometry¹[1].\r\n- **Simulated sensor data per sequence**: This includes 1 x outward-facing RGB camera stream simulated with Aria camera & lens characteristics¹[1].\r\n- **Ground Truth Annotations**: These include 6DoF camera trajectory, 3D floor plan, 2D instance segmentation, and 2D depth map¹[1].\r\n- **Realistic simulated trajectory within each environment**: Device trajectories are simulated within each environment according to a set of rules that mirror how users walk while wearing Project Aria glasses¹[1].\r\n\r\nThis dataset sets a new precedent for the scale of indoor environment datasets and surfaces exciting new research opportunities for tasks related to 3D scene reconstruction, and object detection and tracking¹[1]. It's designed to provide the wider research community with a dataset large enough to surface new challenges and research opportunities²[2].\r\n\r\n(1) Aria Synthetic Environments Dataset | Project Aria. https://www.projectaria.com/datasets/ase/.\r\n(2) Aria Synthetic Environments Dataset | Project Aria Tools. https://facebookresearch.github.io/projectaria_tools/docs/open_datasets/aria_synthetic_environments_dataset.\r\n(3) Project Aria Research | Project Aria. https://www.projectaria.com/research/.","description_withheld":null,"homepage":"https://www.projectaria.com/datasets/ase/","introduced_date":"2024-03-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/scenescript-reconstructing-scenes-with-an","title":"SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model","first_author":"Armen Avetisyan","url":null},"license":null,"modalities":[],"tasks":[{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"},{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"}],"languages":[],"variants":["Aria Synthetic Environments"],"data_loaders":[{"repo":"https://github.com/facebookresearch/ATEK","url":"https://github.com/facebookresearch/ATEK","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-object-detection-on-aria-synthetic","task":"3D Object Detection","dataset_variant":"Aria Synthetic Environments","rows":4,"metrics":["MAP"],"first_row_in_archive_order":{"model":"EVL","paper":"/paper/efm3d-a-benchmark-for-measuring-progress","metrics":{"MAP":"75"},"code_links":[{"title":"facebookresearch/efm3d","url":"https://github.com/facebookresearch/efm3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-reconstruction-on-aria-synthetic","task":"3D Reconstruction","dataset_variant":"Aria Synthetic Environments","rows":1,"metrics":["Accuracy","Completeness","Precision","Recall"],"first_row_in_archive_order":{"model":"EVL","paper":"/paper/efm3d-a-benchmark-for-measuring-progress","metrics":{"Accuracy":"5.7","Completeness":"87.7","Precision":"82.2","Recall":"10.6"},"code_links":[{"title":"facebookresearch/efm3d","url":"https://github.com/facebookresearch/efm3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efm3d-a-benchmark-for-measuring-progress","title":"EFM3D: A Benchmark for Measuring Progress Towards 3D Egocentric Foundation Models","date":"2024-06-14","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}