  This work proposes a self-supervised learning system for segmenting rigid
objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D
videos of static objects, which can be captured with a camera carried by a
mobile robot. A key feature of the self-supervised training process is a
graph-matching algorithm that operates on the over-segmentation output of the
point cloud that is reconstructed from each video. The graph matching, along
with point cloud registration, is able to find reoccurring object patterns
across videos and combine them into 3D object pseudo labels, even under
occlusions or different viewing angles. Projected 2D object masks from 3D
pseudo labels are used to train a pixel-wise feature extractor through
contrastive learning. During online inference, a clustering method uses the
learned features to cluster foreground pixels into object segments. Experiments
highlight the method's effectiveness on both real and synthetic video datasets,
which include cluttered scenes of tabletop objects. The proposed method
outperforms existing unsupervised methods for object segmentation by a large
margin.
