  3D instance segmentation is fundamental to geometric understanding of the
world around us. Existing methods for instance segmentation of 3D scenes rely
on supervision from expensive, manual 3D annotations. We propose UnScene3D, the
first fully unsupervised 3D learning approach for class-agnostic 3D instance
segmentation of indoor scans. UnScene3D first generates pseudo masks by
leveraging self-supervised color and geometry features to find potential object
regions. We operate on a basis of geometric oversegmentation, enabling
efficient representation and learning on high-resolution 3D data. The coarse
proposals are then refined through self-training our model on its predictions.
Our approach improves over state-of-the-art unsupervised 3D instance
segmentation methods by more than 300% Average Precision score, demonstrating
effective instance segmentation even in challenging, cluttered 3D scenes.
