The TYO-L (Toyota Light) dataset is part of the Benchmark for 6D Object Pose Estimation (BOP). Let's delve into the details:

1. TYO-L Dataset Overview:

   • Objects: The TYO-L dataset contains 21 objects.
   • Capture Setup: These objects were captured in multiple poses on a table-top setup.
   • Variations: The dataset includes four different tablecloths and five different lighting conditions.
   • Texture Mapping: The objects are represented by texture-mapped 3D models.
   • License: The dataset is licensed under CC BY-NC 4.0¹.

2. Object Characteristics:

   • The objects in TYO-L exhibit a wide range of sizes.
   • While the dataset focuses on pose estimation, it provides valuable information for research related to low-light image and video enhancement as well ².

3. Benchmark for 6D Object Pose Estimation (BOP):

   • BOP aims to advance the field of 6D object pose estimation by providing standardized datasets, evaluation metrics, and challenges.
   • Other datasets within BOP include LM (Linemod), LM-O (Linemod-Occluded), and T-LESS.
   • 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.
   • The datasets cover a variety of scenarios, including texture-less objects, occlusion, and symmetrical shapes ¹.

In 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.

(1) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/. (2) Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond. https://arxiv.org/html/2212.10772v5. (3) Toyota Dataset | Kaggle. https://www.kaggle.com/datasets/chinmaypradhan29/toyota-dataset. (4) undefined. https://bop.felk.cvut.cz/media/data/bop_datasets.