3D instance segmentation (3DIS) is a crucial task, but point-level
annotations are tedious in fully supervised settings. Thus, using bounding
boxes (bboxes) as annotations has shown great potential. The current mainstream
approach is a two-step process, involving the generation of pseudo-labels from
box annotations and the training of a 3DIS network with the pseudo-labels.
However, due to the presence of intersections among bboxes, not every point has
a determined instance label, especially in overlapping areas. To generate
higher quality pseudo-labels and achieve more precise weakly supervised 3DIS
results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D
Instance Segmentation (BSNet), which devises a novel pseudo-labeler called
Simulation-assisted Transformer. The labeler consists of two main components.
The first is Simulation-assisted Mean Teacher, which introduces Mean Teacher
for the first time in this task and constructs simulated samples to assist the
labeler in acquiring prior knowledge about overlapping areas. To better model
local-global structure, we also propose Local-Global Aware Attention as the
decoder for teacher and student labelers. Extensive experiments conducted on
the ScanNetV2 and S3DIS datasets verify the superiority of our designs. Code is
available at
\href{https://github.com/peoplelu/BSNet}{this https URL}.

提出了一种用于三维实例分割的 Box-Supervised Simulation-assisted Mean Teacher (BSNet) 模型，利用伪标签和模拟样本来提高弱监督下的分割结果的质量和精度。模型包括模拟辅助教师和学生标签器，以更好地建模局部 - 全局结构。通过在 ScanNetV2 和 S3DIS 数据集上进行大量实验验证了模型的优越性。