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Towards holistic understanding of 3D scenes, a general 3D segmentation methodis needed that can segment diverse objects without restrictions on objectquantity or categories, while also reflecting the inherent hierarchicalstructure. To achieve this, we propose OmniSeg3D, an omniversal segmentationmethod aims for segmenting anything in 3D all at once. The key insight is tolift multi-view inconsistent 2D segmentations into a consistent 3D featurefield through a hierarchical contrastive learning framework, which isaccomplished by two steps. Firstly, we design a novel hierarchicalrepresentation based on category-agnostic 2D segmentations to model themulti-level relationship among pixels. Secondly, image features rendered fromthe 3D feature field are clustered at different levels, which can be furtherdrawn closer or pushed apart according to the hierarchical relationship betweendifferent levels. In tackling the challenges posed by inconsistent 2Dsegmentations, this framework yields a global consistent 3D feature field,which further enables hierarchical segmentation, multi-object selection, andglobal discretization. Extensive experiments demonstrate the effectiveness ofour method on high-quality 3D segmentation and accurate hierarchical structureunderstanding. A graphical user interface further facilitates flexibleinteraction for omniversal 3D segmentation.

OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning