{"url":"/dataset/tyo-l","name":"TYO-L","full_name":null,"description_markdown":"The **TYO-L (Toyota Light)** dataset is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let's delve into the details:\r\n\r\n1. **TYO-L Dataset Overview**:\r\n    - **Objects**: The TYO-L dataset contains **21 objects**.\r\n    - **Capture Setup**: These objects were captured in **multiple poses** on a **table-top setup**.\r\n    - **Variations**: The dataset includes **four different tablecloths** and **five different lighting conditions**.\r\n    - **Texture Mapping**: The objects are represented by **texture-mapped 3D models**.\r\n    - **License**: The dataset is licensed under **CC BY-NC 4.0**¹.\r\n\r\n2. **Object Characteristics**:\r\n    - The objects in TYO-L exhibit a **wide range of sizes**.\r\n    - While the dataset focuses on pose estimation, it provides valuable information for research related to **low-light image and video enhancement** as well ².\r\n\r\n3. **Benchmark for 6D Object Pose Estimation (BOP)**:\r\n    - BOP aims to advance the field of 6D object pose estimation by providing standardized datasets, evaluation metrics, and challenges.\r\n    - Other datasets within BOP include **LM (Linemod)**, **LM-O (Linemod-Occluded)**, and **T-LESS**.\r\n    - Each dataset includes **3D object models**, **training/test RGB-D images**, and annotations for **ground-truth 6D object poses**, **2D bounding boxes**, and **2D binary masks**.\r\n    - The datasets cover a variety of scenarios, including texture-less objects, occlusion, and symmetrical shapes ¹.\r\n\r\nIn summary, the TYO-L dataset contributes to the advancement of object pose estimation research, particularly in low-light conditions, and provides valuable resources for the scientific community.\r\n\r\n(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.\r\n(2) Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond. https://arxiv.org/html/2212.10772v5.\r\n(3) Toyota Dataset | Kaggle. https://www.kaggle.com/datasets/chinmaypradhan29/toyota-dataset.\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":"2018-08-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/bop-benchmark-for-6d-object-pose-estimation","title":"BOP: Benchmark for 6D Object Pose Estimation","first_author":"Tomas Hodan","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["TYO-L"],"data_loaders":[],"num_papers_in_archive":12,"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."}