  Unsupervised 3D instance segmentation aims to segment objects from a 3D point
cloud without any annotations. Existing methods face the challenge of either
too loose or too tight clustering, leading to under-segmentation or
over-segmentation. To address this issue, we propose Part2Object, hierarchical
clustering with object guidance. Part2Object employs multi-layer clustering
from points to object parts and objects, allowing objects to manifest at any
layer. Additionally, it extracts and utilizes 3D objectness priors from
temporally consecutive 2D RGB frames to guide the clustering process. Moreover,
we propose Hi-Mask3D to support hierarchical 3D object part and instance
segmentation. By training Hi-Mask3D on the objects and object parts extracted
from Part2Object, we achieve consistent and superior performance compared to
state-of-the-art models in various settings, including unsupervised instance
segmentation, data-efficient fine-tuning, and cross-dataset generalization.
Code is release at https://github.com/ChengShiest/Part2Object
