  Interactive point cloud segmentation has become a pivotal task for
understanding 3D scenes, enabling users to guide segmentation models with
simple interactions such as clicks, therefore significantly reducing the effort
required to tailor models to diverse scenarios and new categories. However, in
the realm of interactive segmentation, the meaning of instance diverges from
that in instance segmentation, because users might desire to segment instances
of both thing and stuff categories that vary greatly in scale. Existing methods
have focused on thing categories, neglecting the segmentation of stuff
categories and the difficulties arising from scale disparity. To bridge this
gap, we propose ClickFormer, an innovative interactive point cloud segmentation
model that accurately segments instances of both thing and stuff categories. We
propose a query augmentation module to augment click queries by a global query
sampling strategy, thus maintaining consistent performance across different
instance scales. Additionally, we employ global attention in the query-voxel
transformer to mitigate the risk of generating false positives, along with
several other network structure improvements to further enhance the model's
segmentation performance. Experiments demonstrate that ClickFormer outperforms
existing interactive point cloud segmentation methods across both indoor and
outdoor datasets, providing more accurate segmentation results with fewer user
clicks in an open-world setting.
