Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding

• URL: http://arxiv.org/abs/2504.19500v1
• Date: Mon, 28 Apr 2025 05:43:14 GMT
• Title: Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding
• Authors: Yan Wang, Baoxiong Jia, Ziyu Zhu, Siyuan Huang,
• Abstract summary: MPEC is a novel learning method for open-vocabulary 3D semantic segmentation.<n>It uses both 3D entity-language alignment and point-entity consistency across different point cloud views.<n>Our method achieves state-of-the-art results on ScanNet for open-vocabulary 3D semantic segmentation.
• Score: 31.40722103849691
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Open-vocabulary 3D scene understanding is pivotal for enhancing physical intelligence, as it enables embodied agents to interpret and interact dynamically within real-world environments. This paper introduces MPEC, a novel Masked Point-Entity Contrastive learning method for open-vocabulary 3D semantic segmentation that leverages both 3D entity-language alignment and point-entity consistency across different point cloud views to foster entity-specific feature representations. Our method improves semantic discrimination and enhances the differentiation of unique instances, achieving state-of-the-art results on ScanNet for open-vocabulary 3D semantic segmentation and demonstrating superior zero-shot scene understanding capabilities. Extensive fine-tuning experiments on 8 datasets, spanning from low-level perception to high-level reasoning tasks, showcase the potential of learned 3D features, driving consistent performance gains across varied 3D scene understanding tasks. Project website: https://mpec-3d.github.io/

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