Title: Unsupervised Object Discovery and Segmentation of RGBD-images,
Abstract: 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.