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.

翻译：3D实例分割是对周围世界进行几何理解的基础。现有的3D场景实例分割方法依赖昂贵的人工3D标注作为监督信息。我们提出UnScene3D，这是首个完全无监督的3D学习方法，用于室内扫描数据的类别无关3D实例分割。UnScene3D首先利用自监督的颜色与几何特征生成伪掩码，以定位潜在物体区域。我们基于几何过分割的操作基础，实现了高效的高分辨率3D数据表征与学习。随后，通过在其预测结果上进行自训练，对粗粒度候选区域进行细化。我们的方法在平均精度分数上比现有最先进的无监督3D实例分割方法提升超过300%，即使在杂乱、具挑战性的3D场景中也能实现有效的实例分割。