  Open-vocabulary 3D instance segmentation is cutting-edge for its ability to
segment 3D instances without predefined categories. However, progress in 3D
lags behind its 2D counterpart due to limited annotated 3D data. To address
this, recent works first generate 2D open-vocabulary masks through 2D models
and then merge them into 3D instances based on metrics calculated between two
neighboring frames. In contrast to these local metrics, we propose a novel
metric, view consensus rate, to enhance the utilization of multi-view
observations. The key insight is that two 2D masks should be deemed part of the
same 3D instance if a significant number of other 2D masks from different views
contain both these two masks. Using this metric as edge weight, we construct a
global mask graph where each mask is a node. Through iterative clustering of
masks showing high view consensus, we generate a series of clusters, each
representing a distinct 3D instance. Notably, our model is training-free.
Through extensive experiments on publicly available datasets, including
ScanNet++, ScanNet200 and MatterPort3D, we demonstrate that our method achieves
state-of-the-art performance in open-vocabulary 3D instance segmentation. Our
project page is at https://pku-epic.github.io/MaskClustering.
