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FreeMask3D: Zero-Shot Point Cloud Instance Segmentation Without 3D Training

文献类型：期刊论文

作者Zhou, Mingquan1,2; Wu, Xiaodong1,2; He, Chen1,2; Wang, Ruiping1,2; Chen, Xilin1,2
刊名IEEE ROBOTICS AND AUTOMATION LETTERS
出版日期2025-12-01
卷号10期号:12页码:12301-12308
关键词Three-dimensional displays Instance segmentation Point cloud compression Semantics Training Solid modeling Annotations Visualization Cameras Adaptation models Object detection segmentation and categorization deep learning for visual perception embodied cognitive science
ISSN号2377-3766
DOI10.1109/LRA.2025.3621977
英文摘要Point cloud instance segmentation is crucial for 3D scene understanding in robotics. However, existing methods heavily rely on learning-based approaches that require large amounts of annotated 3D data, resulting in high annotation costs. Therefore, developing cost-effective and data-efficient solutions is essential. To this end, we propose FreeMask3D, a novel approach that achieves 3D point cloud instance segmentation without requiring any 3D annotation or additional training. Our method consists of two main steps: instance localization and instance recognition. For instance localization, we leverage pre-trained 2D instance segmentation models to perform instance segmentation on corresponding RGB-D images. These results are then mapped to 3D space and fused across frames to generate the final 3D instance masks. For instance recognition, the OpenSem module infers the category of each instance by leveraging the generalization capabilities of cross-modal large models, such as CLIP, to enable open-vocabulary semantic recognition. Experiments and ablation studies on four challenging benchmarks-ScanNetv2, ScanNet200, S3DIS, and Replica-demonstrate that FreeMask3D achieves competitive or superior performance compared to state-of-the-art methods, despite without 3D supervision. Qualitative results highlight its open-vocabulary capabilities based on color, affordance, or uncommon phrase description.
资助项目National Key R&D Program of China[2021ZD0111901] ; National Key R&D Program of China[2023YFF1105104] ; Natural Science Foundation of China[62495082] ; Natural Science Foundation of China[62461160331] ; Natural Science Foundation of China[U21B2025]
WOS研究方向Robotics
语种英语
WOS记录号WOS:001600704200005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/41578]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Ruiping
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab AI Safety, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Mingquan,Wu, Xiaodong,He, Chen,et al. FreeMask3D: Zero-Shot Point Cloud Instance Segmentation Without 3D Training[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2025,10(12):12301-12308.
APA Zhou, Mingquan,Wu, Xiaodong,He, Chen,Wang, Ruiping,&Chen, Xilin.(2025).FreeMask3D: Zero-Shot Point Cloud Instance Segmentation Without 3D Training.IEEE ROBOTICS AND AUTOMATION LETTERS,10(12),12301-12308.
MLA Zhou, Mingquan,et al."FreeMask3D: Zero-Shot Point Cloud Instance Segmentation Without 3D Training".IEEE ROBOTICS AND AUTOMATION LETTERS 10.12(2025):12301-12308.

入库方式： OAI收割

来源：计算技术研究所

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