In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images. The system models the sensor noise directly from data, allowing accurate segmentation without sensor specific hand tuning of measurement noise models making use of the recently introduced Statistical Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the system is able to apply probabilistic inference, enabling reliable segmentation in previously challenging scenarios. In addition, we introduce new methods for filtering out false positives, significantly improving the signal to noise ratio. We show that the system significantly outperform state-of-the-art in on a challenging real-world dataset.
@article{arxiv.1710.06929,
title = {Unsupervised Object Discovery and Segmentation of RGBD-images},
author = {Johan Ekekrantz and Nils Bore and Rares Ambrus and John Folkesson and Patric Jensfelt},
journal= {arXiv preprint arXiv:1710.06929},
year = {2017}
}