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.

本文提出了一种新的方法，通过利用二维预训练模型，将二维 segments 抬升为三维，并使用神经场表示将它们融合在一起，实现多层面的一致性，并提出了一个可扩展并适合于大量对象的场慢的聚类方法。通过新建的混乱房间数据集，以及 ScanNet、Hypersim 和 Replica 数据集上的实验，证明了该方法的有效性和可扩展性。