Large-scale datasets with point-wise semantic and instance labels are crucial
to 3D instance segmentation but also expensive. To leverage unlabeled data,
previous semi-supervised 3D instance segmentation approaches have explored
self-training frameworks, which rely on high-quality pseudo labels for
consistency regularization. They intuitively utilize both instance and semantic
pseudo labels in a joint learning manner. However, semantic pseudo labels
contain numerous noise derived from the imbalanced category distribution and
natural confusion of similar but distinct categories, which leads to severe
collapses in self-training. Motivated by the observation that 3D instances are
non-overlapping and spatially separable, we ask whether we can solely rely on
instance consistency regularization for improved semi-supervised segmentation.
To this end, we propose a novel self-training network InsTeacher3D to explore
and exploit pure instance knowledge from unlabeled data. We first build a
parallel base 3D instance segmentation model DKNet, which distinguishes each
instance from the others via discriminative instance kernels without reliance
on semantic segmentation. Based on DKNet, we further design a novel instance
consistency regularization framework to generate and leverage high-quality
instance pseudo labels. Experimental results on multiple large-scale datasets
show that the InsTeacher3D significantly outperforms prior state-of-the-art
semi-supervised approaches. Code is available:
this https URL

提出了一种新的自我训练网络 InsTeacher3D，用于从无标签数据中探索和利用纯实例知识，通过实例一致性正则化实现半监督的 3D 实例分割，结果显示 InsTeacher3D 明显优于先前最先进的半监督方法。