{"url":"/dataset/fraunhofer-ipa-bin-picking","name":"Fraunhofer IPA Bin-Picking","full_name":"Fraunhofer IPA Bin-Picking","description_markdown":"The **Fraunhofer IPA Bin-Picking** dataset is a large-scale dataset comprising both simulated and real-world scenes for various objects (potentially having symmetries) and is fully annotated with 6D poses. A pyhsics simulation is used to create scenes of many parts in bulk by dropping objects in a random position and orientation above a bin. Additionally, this dataset extends the Siléane dataset by providing more samples. This allows to e.g. train deep neural networks and benchmark the performance on the public Siléane dataset\n\nSource: [https://www.bin-picking.ai/en/dataset.html](https://www.bin-picking.ai/en/dataset.html)\nImage Source: [https://arxiv.org/abs/1912.12125](https://arxiv.org/abs/1912.12125)","description_withheld":null,"homepage":"https://www.bin-picking.ai/en/dataset.html","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-6d-object-pose-estimation-dataset","title":"Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking","first_author":"Kilian Kleeberger","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"6D","url":"/datasets/modality/6d"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"6D Pose Estimation using RGB","url":"/task/6d-pose-estimation","datasets_with_task":"/datasets/task/6d-pose-estimation"}],"languages":[],"variants":["Fraunhofer IPA Bin-Picking"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"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."}