Authors:
Aleksi Ikkala
;
Joni Pajarinen
and
Ville Kyrki
Affiliation:
Aalto University, Finland
Keyword(s):
Object Segmentation, RGB-D Segmentation, Benchmarking, Dataset, Complex Objects, Real World Objects.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Pattern Recognition
;
Robotics
;
Segmentation and Grouping
;
Software Engineering
Abstract:
In this paper we present a new RGB-D dataset captured with the Kinect sensor. The dataset is composed
of typical children’s toys and contains a total of 449 RGB-D images alongside with their annotated ground
truth images. Compared to existing RBG-D object segmentation datasets, the objects in our proposed dataset
have more complex shapes and less texture. The images are also crowded and thus highly occluded. Three
state-of-the-art segmentation methods are benchmarked using the dataset. These methods attack the problem
of object segmentation from different starting points, providing a comprehensive view on the properties of
the proposed dataset as well as the state-of-the-art performance. The results are mostly satisfactory but there
remains plenty of room for improvement. This novel dataset thus poses the next challenge in the area of
RGB-D object segmentation.