21 datasets found

A method for generating large customized datasets for object 6D pose estimation, supporting transparent or translucent objects.

The dataset used in the paper is the LINEMOD and Occlusion LINEMOD datasets, which are used for 6D object pose estimation.

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties,...

Cat Head is a dataset containing 9k images of cat heads each annotated with 7 landmarks.

The Scan2CAD dataset contains object-level human-generated annotations. The annotations include category label, segmentation, a similar CAD model in ShapeNet, and the...

The YCB-V dataset is used for testing the class-adaptive object detector. It contains 33 objects with a large number of occurrences.

The dataset used for testing the proposed self-supervised 6D object pose estimation approach.

The YCB-V dataset contains 21 objects with complex shapes and occlusions.

The LM-O dataset consists of 1214 images and is used as a test dataset for 6DoF object pose estimation. The dataset includes 8 objects with partial occlusion.

The TOD dataset is a public stereo object pose estimation dataset that includes about 45,000 stereo images of 15 different objects, along with the ground truth segmentation...

The Transparent Tableware Dataset (TTD) is a dataset made available with this paper that includes 600 stereo images of real scenes with some objects positioned in the scene,...

The YCB-Video dataset contains 92 videos of 21 objects with varying textures and sizes under cluttered indoor environments.

The LMO dataset is a subset of the LM dataset consisting of eight objects in more cluttered scenes.

The LM consists of 13 objects with approximately 1.2K images per object. We follow the settings described in [2], which uses 15% of the data for training and the rest for testing.

LINEMOD dataset is a widely used instance-level 6D object pose estimation dataset which consists of 13 different objects with significant shape variation.

The dataset used in the paper for 6Dof object pose estimation using RGB-D images.

Dataset for 6D object pose estimation and refinement.

The Honnotate dataset is a method for 3D annotation of hand and object poses.

The PoseCNN dataset is a dataset for 6D object pose estimation in cluttered scenes.

The BOP Challenge 2022 is the fourth in a series of public challenges that are part of the BOP1 project aiming to continuously report the state of the art in 6D object pose...