{"url":"/dataset/ic-mi","name":"IC-MI","full_name":null,"description_markdown":"The **IC-MI dataset**, introduced by **Tejani et al.**, is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let's delve into the details:\r\n\r\n1. **Dataset Overview**:\r\n   - The IC-MI dataset contains **models of two texture-less and four textured household objects**.\r\n   - These objects serve as test cases for evaluating 6D object detection and pose estimation methods.\r\n   - The test images showcase **multiple object instances** with **clutter** and **slight occlusion**¹².\r\n\r\n2. **Object Categories**:\r\n   - **Texture-less Objects**: Two texture-less objects are included in the dataset.\r\n   - **Textured Objects**: Four textured household objects are part of the dataset.\r\n\r\n3. **Ground-Truth Annotations**:\r\n   - The dataset provides **training/test RGB-D images** that are annotated with the following ground-truth information:\r\n     - **6D object poses**: Precise 3D positions and orientations of the objects.\r\n     - **2D bounding boxes**: Enclosing the objects in the 2D image plane.\r\n     - **2D binary masks**: Indicating the object pixels.\r\n\r\n4. **Data Creation**:\r\n   - The **3D object models** were manually created or reconstructed using systems similar to **KinectFusion**.\r\n   - Training images were captured by **RGB-D/Gray-D sensors** or rendered from the 3D models.\r\n   - All test images are real-world captures.\r\n\r\n5. **Format and Storage**:\r\n   - The datasets are provided in the **BOP format**.\r\n   - The BOP toolkit expects datasets to be stored in the same folder, with each dataset in a subfolder named after its base name (e.g., \"lm,\" \"lmo,\" \"tless\").\r\n   - The IC-MI dataset is one of the components of this comprehensive benchmark.\r\n\r\nIn summary, the IC-MI dataset offers valuable resources for advancing 6D object pose estimation research, particularly in scenarios involving texture-less and textured objects with clutter and mild occlusion¹.\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 | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-030-01249-6_2.\r\n(3) Sensors | Free Full-Text | Visual Attention and Color Cues for ... - MDPI. https://www.mdpi.com/1424-8220/21/23/8090.\r\n(4) 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":["IC-MI"],"data_loaders":[],"num_papers_in_archive":0,"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."}