  During interactive segmentation, a model and a user work together to
delineate objects of interest in a 3D point cloud. In an iterative process, the
model assigns each data point to an object (or the background), while the user
corrects errors in the resulting segmentation and feeds them back into the
model. The current best practice formulates the problem as binary
classification and segments objects one at a time. The model expects the user
to provide positive clicks to indicate regions wrongly assigned to the
background and negative clicks on regions wrongly assigned to the object.
Sequentially visiting objects is wasteful since it disregards synergies between
objects: a positive click for a given object can, by definition, serve as a
negative click for nearby objects. Moreover, a direct competition between
adjacent objects can speed up the identification of their common boundary. We
introduce AGILE3D, an efficient, attention-based model that (1) supports
simultaneous segmentation of multiple 3D objects, (2) yields more accurate
segmentation masks with fewer user clicks, and (3) offers faster inference. Our
core idea is to encode user clicks as spatial-temporal queries and enable
explicit interactions between click queries as well as between them and the 3D
scene through a click attention module. Every time new clicks are added, we
only need to run a lightweight decoder that produces updated segmentation
masks. In experiments with four different 3D point cloud datasets, AGILE3D sets
a new state-of-the-art. Moreover, we also verify its practicality in real-world
setups with real user studies.
