HO-3D is a dataset with 3D pose annotations for hand and object under severe occlusions from each other.
The 68 sequences in the dataset contain 10 different persons manipulating 10 different objects, which
are taken from YCB objects dataset. The dataset currently contains annotations for 77,558 images which are
split into 66,034 training images (from 55 sequences) and 11,524 evaluation images (from 13 sequences). The
evaluation sequences are carefully selected to address the following scenarios:
- Seen object and seen hand: Sequences SM1, SB11 and SB13 contain hands and objects which are also
used in the training set.
- Unseen object and seen hand: Sequences AP10, AP11, AP12, AP13 and AP14 contain
019_pitcher_base object which is not used in the training set.
- Seen object and unseen hand: Sequences MPM10, MPM11, MPM12, MPM13 and MPM14 contain
a subject with different hand shape and color andis not part of the training set.
In order to evaluate different methods for hand pose estimation from RGB/depth images on our dataset using a
common protocol, we have launched a codalab competition. The online competition server evaluates hand pose
estimation results from different methods using three different standard metrics (see our paper) and can
be used to compare with other submissions. The hand pose annotations for the evaluation split are withheld,
while the object pose annotations are made public. A set of additional information is provided for the
evaluation split to aid the pose estimation task (see evaluation
page in competition). Evaluation
scripts used in the challenge are available in the github repo provided below.