Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking   In this paper, we introduce a new public dataset for 6D object pose
estimation and instance segmentation for industrial bin-picking. The dataset
comprises both synthetic and real-world scenes. For both, point clouds, depth
images, and annotations comprising the 6D pose (position and orientation), a
visibility score, and a segmentation mask for each object are provided. Along
with the raw data, a method for precisely annotating real-world scenes is
proposed. To the best of our knowledge, this is the first public dataset for 6D
object pose estimation and instance segmentation for bin-picking containing
sufficiently annotated data for learning-based approaches. Furthermore, it is
one of the largest public datasets for object pose estimation in general. The
dataset is publicly available at http://www.bin-picking.ai/en/dataset.html.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph