{"url":"/dataset/lm-o","name":"LM-O","full_name":"LINEMOD-Occluded","description_markdown":"The **LM-O (Linemod-Occluded)** dataset, introduced by Brachmann et al. in their work on **6D object pose estimation**, provides additional ground-truth annotations for all modeled objects in one of the test sets from the **Linemod (LM)** dataset. This extension introduces challenging test cases with various levels of **occlusion** ¹²³.\r\n\r\nHere are the key details about the LM-O dataset:\r\n\r\n- **Objective**: The primary goal of the LM-O dataset is to evaluate the performance of 6D object pose estimation methods under occlusion conditions.\r\n- **Source**: The dataset builds upon the Linemod dataset, which was originally proposed by Hinterstoisser et al. in their work on model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes ¹.\r\n- **Annotations**: LM-O provides additional ground-truth annotations for all other instances of the modeled objects in one of the Linemod test sets. These annotations include information related to occlusion, making it a valuable resource for assessing pose estimation algorithms in challenging scenarios ¹.\r\n- **Test Cases**: The LM-O dataset includes test images showing one annotated object instance with significant clutter but only mild occlusion. By incorporating occluded instances, it simulates real-world scenarios where objects may be partially hidden or obscured ¹.\r\n- **License**: The dataset is available under the **CC BY-SA 4.0** license, allowing researchers to use and build upon it for their own investigations ¹.\r\n\r\nResearchers and practitioners can leverage the LM-O dataset to develop and evaluate robust 6D object pose estimation methods that can handle occluded objects effectively. It serves as a valuable benchmark for advancing the field of computer vision and robotics.\r\n\r\n(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.\r\n(2) BOP: Benchmark for 6D Object Pose Estimation - cvut.cz. https://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_slides_eccv.pdf.\r\n(3) BOP: Benchmark for 6D Object Pose Estimation | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-030-01249-6_2.\r\n(4) Recovering 6D Object Pose: A Review and Multi-modal Analysis. https://link.springer.com/chapter/10.1007/978-3-030-11024-6_2.\r\n(5) undefined. https://bop.felk.cvut.cz/media/data/bop_datasets.","description_withheld":null,"homepage":"https://bop.felk.cvut.cz/datasets","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["LM-O"],"data_loaders":[],"num_papers_in_archive":1,"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."}