{"url":"/dataset/mvtec-itodd","name":"MVTec ITODD","full_name":null,"description_markdown":"The **MVTec Industrial 3D Object Detection Dataset (MVTec ITODD)**, introduced by Bertram Drost, Markus Ulrich, Paul Bergmann, and Carsten Steger from MVTec Software GmbH, is a valuable resource for 3D object detection and pose estimation in industrial contexts¹²³. Here are the key details about this dataset:\r\n\r\n1. **Purpose and Focus**:\r\n   - MVTec ITODD is specifically designed for **realistic industrial setups**.\r\n   - Unlike other 3D object detection datasets that often represent everyday life scenarios or mobile robot environments, ITODD models tasks relevant to industrial applications, such as **bin picking** and **object inspection**.\r\n   - The dataset emphasizes objects, settings, and requirements that align with the challenges faced in industrial contexts.\r\n\r\n2. **Dataset Characteristics**:\r\n   - Contains **28 objects** with varying characteristics.\r\n   - Arranged in over **800 scenes**.\r\n   - Labeled with approximately **3500 rigid 3D transformations** of the object instances as ground truth.\r\n   - Captures different **modalities** by using **two industrial 3D sensors** and **three high-resolution grayscale cameras** observing the scene from various angles.\r\n\r\n3. **Evaluation Criteria**:\r\n   - Unlike purely performance-based criteria, ITODD focuses on practical aspects:\r\n     - **Runtimes**\r\n     - **Memory consumption**\r\n     - **Useful correctness measurements**\r\n     - **Accuracy**\r\n\r\n4. **Method Evaluation**:\r\n   - The dataset has been evaluated using **five different methods**, revealing room for improvement.\r\n   - Researchers are encouraged to submit their results for evaluation and inclusion in the dataset's result lists on the [official website](https://www.mvtec.com/company/research/datasets/itodd)¹.\r\n\r\nIn summary, MVTec ITODD provides a valuable benchmark for developing and evaluating 3D object detection algorithms tailored to industrial scenarios¹. Researchers can use this dataset to address the unique challenges posed by real-world industrial applications.\r\n\r\n(1) Introducing MVTec ITODD - A Dataset for 3D Object Recognition in Industry. https://www.mvtec.com/fileadmin/Redaktion/mvtec.com/company/research/datasets/mvtec_itodd.pdf.\r\n(2) ICCV 2017 Open Access Repository. https://openaccess.thecvf.com/content_ICCV_2017_workshops/w31/html/Drost_Introducing_MVTec_ITODD_ICCV_2017_paper.html.\r\n(3) (PDF) Introducing MVTec ITODD — A Dataset for 3D Object Recognition in .... https://typeset.io/papers/introducing-mvtec-itodd-a-dataset-for-3d-object-recognition-2np8emo2oc.\r\n(4) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.\r\n(5) undefined. http://www.mvtec.com.","description_withheld":null,"homepage":"https://www.mvtec.com/company/research/datasets/mvtec-itodd","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["MVTec ITODD"],"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."}