Talks and Poster Presentations (with Proceedings-Entry):

R. Monica, J. Aleotti, M. Zillich, M. Vincze:
"Multi-Label Point Cloud Annotation by Selection of Sparse Control Points";
Talk: IEEE International Conference on 3D Vision (3DV), Qingdao, China; 10-10-2017 - 10-12-2017; in: "3DV 2017", (2017), 8 pages.

English abstract:
This paper presents a user-friendly approach for multilabel
point cloud annotation. The method requires the
user to select sparse control points belonging to the objects
through a mouse-based interface. Multiple control points
may be assigned to the same label. The software utilizes
the selected control points to perform a segmentation algorithm
on the neighborhood graph, based on shortest path
tree. The user is provided a real-time feedback about the
result, and can correct segmentation errors. In contrast
to previous work the method supports multi-label annotation
of unorganized point clouds. The method has been
evaluated by multiple users and compared with a standard
rectangle-based selection technique. Results indicate that
the proposed method is perceived as easier to use, and that
it allows a faster segmentation even in complex scenarios
with occlusions.

Created from the Publication Database of the Vienna University of Technology.