Datasets

The datasets include 3D object models and training and test RGB-D images annotated with ground-truth 6D object poses and intrinsic camera parameters.

The 3D object models were created manually or using KinectFusion-like systems for 3D surface reconstruction. The training images show individual objects from different viewpoints and are either captured by an RGB-D/Gray-D sensor or obtained by rendering of the 3D object models. The test images were captured in scenes with graded complexity, often with clutter and occlusion.

The datasets are provided in the BOP format. The BOP toolkit expects all datasets to be stored in the same folder, each dataset in a subfolder named with the base name of the dataset (e.g. "lm", "lmo", "tless").

An example showing how to download and unpack the LM dataset from bash (names of archives with the other datasets can be seen in the download links below):

export SRC=http://ptak.felk.cvut.cz/6DB/public/bop_datasets
wget $SRC/lm_base.zip         # Base archive with dataset info, camera parameters, etc.
wget $SRC/lm_models.zip       # 3D object models.
wget $SRC/lm_test_all.zip     # All test images ("_bop19" for a subset used in the BOP Challenge 2019/2020).
wget $SRC/lm_train_pbr.zip    # PBR training images (rendered with BlenderProc4BOP).

unzip lm_base.zip             # Contains folder "lm".
unzip lm_models.zip -d lm     # Unpacks to "lm".
unzip lm_test_all.zip -d lm   # Unpacks to "lm".
unzip lm_train_pbr.zip -d lm  # Unpacks to "lm".

LM (Linemod)

Hinterstoisser et al.: Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes, ACCV 2012, project website, license: CC BY 4.0.

15 texture-less household objects with discriminative color, shape and size. Each object is associated with a test image set showing one annotated object instance with significant clutter but only mild occlusion.

LM-O (Linemod-Occluded)

Brachmann et al.: Learning 6d object pose estimation using 3d object coordinates, ECCV 2014, project website, license: CC BY-SA 4.0.

Provides additional ground-truth annotations for all modeled objects in one of the test sets from LM. This introduces challenging test cases with various levels of occlusion. Note the PBR-BlenderProc4BOP training images are the same as for LM.

T-LESS

Hodan et al.: T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects, WACV 2017, project website, license: CC BY 4.0.

30 industry-relevant objects with no significant texture or discriminative color. The objects exhibit symmetries and mutual similarities in shape and/or size, and a few objects are a composition of other objects. Test images originate from 20 scenes with varying complexity. Only images from Primesense Carmine 1.09 are included in the archives below. Images from Microsoft Kinect 2 and Canon IXUS 950 IS are available at the project website. However, only the Primesense images can be used in the BOP Challenge 2019/2020.

ITODD (MVTec ITODD)

Drost et al.: Introducing MVTec ITODD - A Dataset for 3D Object Recognition in Industry, ICCVW 2017, project website, license: CC BY-NC-SA 4.0.

28 objects captured in realistic industrial setups with a high-quality Gray-D sensor. The ground-truth 6D poses are publicly available only for the validation images, not for the test images.

HB (HomebrewedDB)

Kaskman et al.: HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects, ICCVW 2019, project website, license: CC0 1.0 Universal.

33 objects (17 toy, 8 household and 8 industry-relevant objects) captured in 13 scenes with varying complexity. The ground-truth 6D poses are publicly available only for the validation images, not for the test images. The dataset includes images from Primesense Carmine 1.09 and Microsoft Kinect 2. Note that only the Primesense images can be used in the BOP Challenge 2019/2020.

HOPE (NVIDIA Household Objects for Pose Estimation)

Tyree et al.: 6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark. 2020, project website, license: CC BY-NC-SA 4.0 license.

28 toy grocery objects are captured in 50 scenes from 10 household/office environments. Up to 5 lighting variations are captured for each scene, including backlighting and angled direct lighting with cast shadows. Scenes are cluttered with varying levels of occlusion. The collection of toy objects is available from online retailers for about 50 USD (see "dataset_info.md" in the base archive for details).

YCB-V (YCB-Video)

Xiang et al.: PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes, RSS 2018, project website, license: MIT.

21 YCB objects captured in 92 videos. Compared to the original YCB-Video dataset, the differences in the BOP version are:

  • Only on a subset of test images is used for the evaluation - a subset of 75 images was manually selected for each of the 12 test scenes to remove redundancies and to avoid images with erroneous ground-truth poses. The list of selected images is in the file test_targets_bop19.json in the base archive. The selected images are a subset of images listed in "YCB_Video_Dataset/image_sets/keyframe.txt" in the original dataset.
  • The 3D models were converted from meters to millimeters and the centers of their 3D bounding boxes were aligned with the origin of the model coordinate system. This transformation was reflected also in the ground-truth poses. The models were transformed so they follow the same conventions as models from other datasets included in BOP and are thus compatible with the BOP toolkit.
  • We additionally provide 50K PBR training images that were generated for the BOP Challenge 2020.

The 80K synthetic training images included in the original version of the dataset are also provided.

RU-APC (Rutgers APC)

Rennie et al.: A dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place, Robotics and Automation Letters 2016, project website.

14 textured products from the Amazon Picking Challenge 2015 [6], each associated with test images of a cluttered warehouse shelf.

IC-BIN (Doumanoglou et al.)

Doumanoglou et al.: Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd, CVPR 2016, project website.

Test images of two objects from IC-MI, which appear in multiple locations with heavy occlusion in a bin-picking scenario.

IC-MI (Tejani et al.)

Tejani et al.: Latent-class hough forests for 3D object detection and pose estimation, ECCV 2014, project website.

Two texture-less and four textured household objects. The test images show multiple object instances with clutter and slight occlusion.

TUD-L (TUD Light)

Hodan, Michel et al.: BOP: Benchmark for 6D Object Pose Estimation, ECCV 2018.

Training and test image sequences show three moving objects under eight lighting conditions.

TYO-L (Toyota Light)

Hodan, Michel et al.: BOP: Benchmark for 6D Object Pose Estimation, ECCV 2018.

21 objects, each captured in multiple poses on a table-top setup, with four different table cloths and five different lighting conditions.

The thumbnails of the datasets were obtained by rendering colored 3D object models in the ground truth 6D poses over darkened test images.