Unsupervised Object Discovery and Segmentation of RGBD-images
This addresses the problem of automated object segmentation without manual tuning for robotics or computer vision applications, though it appears incremental as it builds on existing methods.
The paper tackles unsupervised object discovery and segmentation in RGBD images by modeling sensor noise directly from data and using probabilistic inference, achieving significantly better performance than state-of-the-art methods on a challenging real-world dataset.
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.