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Instance segmentation in 3D is a challenging task due to the lack oflarge-scale annotated datasets. In this paper, we show that this task can beaddressed effectively by leveraging instead 2D pre-trained models for instancesegmentation. We propose a novel approach to lift 2D segments to 3D and fusethem by means of a neural field representation, which encourages multi-viewconsistency across frames. The core of our approach is a slow-fast clusteringobjective function, which is scalable and well-suited for scenes with a largenumber of objects. Unlike previous approaches, our method does not require anupper bound on the number of objects or object tracking across frames. Todemonstrate the scalability of the slow-fast clustering, we create a newsemi-realistic dataset called the Messy Rooms dataset, which features sceneswith up to 500 objects per scene. Our approach outperforms the state-of-the-arton challenging scenes from the ScanNet, Hypersim, and Replica datasets, as wellas on our newly created Messy Rooms dataset, demonstrating the effectivenessand scalability of our slow-fast clustering method.

Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion