  Instance segmentation in 3D is a challenging task due to the lack of
large-scale annotated datasets. In this paper, we show that this task can be
addressed effectively by leveraging instead 2D pre-trained models for instance
segmentation. We propose a novel approach to lift 2D segments to 3D and fuse
them by means of a neural field representation, which encourages multi-view
consistency across frames. The core of our approach is a slow-fast clustering
objective function, which is scalable and well-suited for scenes with a large
number of objects. Unlike previous approaches, our method does not require an
upper bound on the number of objects or object tracking across frames. To
demonstrate the scalability of the slow-fast clustering, we create a new
semi-realistic dataset called the Messy Rooms dataset, which features scenes
with up to 500 objects per scene. Our approach outperforms the state-of-the-art
on challenging scenes from the ScanNet, Hypersim, and Replica datasets, as well
as on our newly created Messy Rooms dataset, demonstrating the effectiveness
and scalability of our slow-fast clustering method.
