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arXiv · 2504.19500

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

Yan Wang·Baoxiong Jia·Ziyu Zhu·Siyuan Huang
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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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Yan Wang, Baoxiong Jia, Ziyu Zhu, Siyuan Huang. 2025-04-28. Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding. https://arxiv.org/abs/2504.19500

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