Segment Any 3D-Part in a Scene from a Sentence

• URL: http://arxiv.org/abs/2506.19331v1
• Date: Tue, 24 Jun 2025 05:51:22 GMT
• Title: Segment Any 3D-Part in a Scene from a Sentence
• Authors: Hongyu Wu, Pengwan Yang, Yuki M. Asano, Cees G. M. Snoek,
• Abstract summary: This paper aims to achieve the segmentation of any 3D part in a scene based on natural language descriptions.<n>We introduce the 3D-PU dataset, the first large-scale 3D dataset with dense part annotations.<n>On the methodological side, we propose OpenPart3D, a 3D-input-only framework to tackle the challenges of part-level segmentation.
• Score: 50.46950922754459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper aims to achieve the segmentation of any 3D part in a scene based on natural language descriptions, extending beyond traditional object-level 3D scene understanding and addressing both data and methodological challenges. Due to the expensive acquisition and annotation burden, existing datasets and methods are predominantly limited to object-level comprehension. To overcome the limitations of data and annotation availability, we introduce the 3D-PU dataset, the first large-scale 3D dataset with dense part annotations, created through an innovative and cost-effective method for constructing synthetic 3D scenes with fine-grained part-level annotations, paving the way for advanced 3D-part scene understanding. On the methodological side, we propose OpenPart3D, a 3D-input-only framework to effectively tackle the challenges of part-level segmentation. Extensive experiments demonstrate the superiority of our approach in open-vocabulary 3D scene understanding tasks at the part level, with strong generalization capabilities across various 3D scene datasets.

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