3D semantic scene understanding tasks have achieved great success with the
emergence of deep learning, but often require a huge amount of manually
annotated training data. To alleviate the annotation cost, we propose the first
weakly-supervised 3D instance segmentation method that only requires
categorical semantic labels as supervision, and we do not need instance-level
labels. The required semantic annotations can be either dense or extreme sparse
(e.g. 0.02% of total points). Even without having any instance-related
ground-truth, we design an approach to break point clouds into raw fragments
and find the most confident samples for learning instance centroids.
Furthermore, we construct a recomposed dataset using pseudo instances, which is
used to learn our defined multilevel shape-aware objectness signal. An
asymmetrical object inference algorithm is followed to process core points and
boundary points with different strategies, and generate high-quality pseudo
instance labels to guide iterative training. Experiments demonstrate that our
method can achieve comparable results with recent fully supervised methods. By
generating pseudo instance labels from categorical semantic labels, our
designed approach can also assist existing methods for learning 3D instance
segmentation at reduced annotation cost.

通过使用弱监督的三维实例分割方法，只需要分类语义标签作为监督，而无需实例级别标签，从而减少批注成本，并利用伪实例标签辅助现有方法学习三维实例分割。

无实例级注释的弱监督三维实例分割

Weakly Supervised 3D Instance Segmentation without Instance-level  Annotations

Weakly-supervised instance segmentation (WSIS) has been considered as a more
challenging task than weakly-supervised semantic segmentation (WSSS). Compared
to WSSS, WSIS requires instance-wise localization, which is difficult to
extract from image-level labels. To tackle the problem, most WSIS approaches
use off-the-shelf proposal techniques that require pre-training with instance
or object level labels, deviating the fundamental definition of the
fully-image-level supervised setting. In this paper, we propose a novel
approach including two innovative components. First, we propose a semantic
knowledge transfer to obtain pseudo instance labels by transferring the
knowledge of WSSS to WSIS while eliminating the need for the off-the-shelf
proposals. Second, we propose a self-refinement method to refine the pseudo
instance labels in a self-supervised scheme and to use the refined labels for
training in an online manner. Here, we discover an erroneous phenomenon,
semantic drift, that occurred by the missing instances in pseudo instance
labels categorized as background class. This semantic drift occurs confusion
between background and instance in training and consequently degrades the
segmentation performance. We term this problem as semantic drift problem and
show that our proposed self-refinement method eliminates the semantic drift
problem. The extensive experiments on PASCAL VOC 2012 and MS COCO demonstrate
the effectiveness of our approach, and we achieve a considerable performance
without off-the-shelf proposal techniques. The code is available at
this https URL

本文提出一种创新的方法来解决弱监督实例分割任务中的实例定位难题，并使用自我训练方案来改进伪标注，消除语义漂移问题。通过在 PASCAL VOC 2012 和 MS COCO 上广泛实验，证明了该方法的有效性，无需使用与实例或对象有关的预先训练技术。