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This research presents OV-MAP, a method that enables mobile robots to perform zero-shot 3D instance segmentation by projecting class-agnostic 2D masks into three-dimensional space. By integrating open features and synthesizing depth from point clouds, the approach allows robots to recognize objects in diverse environments without relying on supervised 3D training data. The technique addresses voxel boundary leakage issues while demonstrating robustness across both simulated datasets like ScanNet200 and real-world scenarios.
OV-MAP: Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots
arXiv:2506.11585v2 Announce Type: replace-cross Abstract: We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities. A significant challenge arises when overlapping features from adjacent voxels reduce instance-level precision, as features spill over voxel boundaries, blending neighboring regions together. Our method overcomes this by employing a class-agnostic segmentation model to project 2D masks into 3D space, combined with a supplemented depth image created by merging raw and synthetic depth from point clouds. This approach, along with a 3D mask voting mechanism, enables accurate zero-shot 3D instance segmentation without relying on 3D supervised segmentation models. We assess the effectiveness of our method through comprehensive experiments on public datasets such as ScanNet200 and Replica, demonstrating superior zero-shot performance, robustness, and adaptability across diverse environments. Additionally, we conducted real-world experiments to demonstrate our method's adaptability and robustness when applied to diverse real-world environments.
Abstract:We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities. A significant challenge arises when overlapping features from adjacent voxels reduce instance-level precision, as features spill over voxel boundaries, blending neighboring regions together. Our method overcomes this by employing a class-agnostic segmentation model to project 2D masks into 3D space, combined with a supplemented depth image created by merging raw and synthetic depth from point clouds. This approach, along with a 3D mask voting mechanism, enables accurate zero-shot 3D instance segmentation without relying on 3D supervised segmentation models. We assess the effectiveness of our method through comprehensive experiments on public datasets such as ScanNet200 and Replica, demonstrating superior zero-shot performance, robustness, and adaptability across diverse environments. Additionally, we conducted real-world experiments to demonstrate our method's adaptability and robustness when applied to diverse real-world environments.
7 pages, 7 figures. Published in the Proceedings of the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2506.11585 [cs.CV]
(or arXiv:2506.11585v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2506.11585 Focus to learn more arXiv-issued DOI via DataCite
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, pp. 13780-13786
https://doi.org/10.1109/IROS58592.2024.10801841 Focus to learn more DOI(s) linking to related resources
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