{"url":"/dataset/tud-l","name":"TUD-L","full_name":null,"description_markdown":"The **TUD-L (TUD Light)** dataset is part of the **Benchmark for 6D Object Pose Estimation (BOP)**. Let me provide you with some details about this dataset:\r\n\r\n1. **Description**:\r\n   - The TUD-L dataset focuses on **lighting conditions** and includes **three moving objects** observed under **eight different illumination scenarios**.\r\n   - It was specifically created as part of the BOP benchmark to evaluate 6D object pose estimation methods.\r\n\r\n2. **Object Instances**:\r\n   - The dataset contains three objects:\r\n     - **Dragon**\r\n     - **Frog**\r\n     - **Watering pot**\r\n\r\n3. **Challenges**:\r\n   - TUD-L introduces **challenging illumination variations** to test the robustness of pose estimation algorithms.\r\n   - Unlike other datasets, TUD-L has **limited clutter and occlusion**, making it suitable for evaluating lighting-specific performance.\r\n\r\n4. **Ground-Truth Annotations**:\r\n   - The dataset provides **ground-truth 6D object poses**, **2D bounding boxes**, and **2D binary masks** for the training and test RGB-D images.\r\n   - These annotations enable researchers to evaluate their algorithms accurately.\r\n\r\n5. **Usage**:\r\n   - Researchers can use the TUD-L dataset to develop and evaluate 6D object pose estimation methods under varying lighting conditions.","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":["TUD-L"],"data_loaders":[],"num_papers_in_archive":34,"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."}