Rüthlein M, Köferl F, Mehringer W, Eskofier B (2020)
Publication Type: Conference contribution, other
Publication year: 2020
Event location: Virtual Conference
Human segmentation of point clouds for the creation of datasets for deep learning is a tedious
and especially time-consuming task. Interactive segmentation methods from the domain of
RGB images reduce this time effort by using an
iterative scheme of deep neural networks and human labelers. We apply this interactive segmentation scheme to the point cloud domain, using
PointNet as our backbone and exploiting user
marker-information by label propagation. The
evaluation was based on a user study and suggests a significant increase in segmentation speed,
which is traded off for mask quality. On average, our approach decreased segmentation time
by 21.1%. We consider this work as an important first step into the domain of interactive point
cloud segmentation.
APA:
Rüthlein, M., Köferl, F., Mehringer, W., & Eskofier, B. (2020). Interactive Segmentation of RGB-D Indoor Scenes using Deep Learning. In Proceedings of the International Conference on Machine Learning; 2nd ICML 2020 Workshop on Human in the Loop Learning. Virtual Conference.
MLA:
Rüthlein, Maximilian, et al. "Interactive Segmentation of RGB-D Indoor Scenes using Deep Learning." Proceedings of the International Conference on Machine Learning; 2nd ICML 2020 Workshop on Human in the Loop Learning, Virtual Conference 2020.
BibTeX: Download