OTAS: Open-vocabulary Token Alignment for Outdoor Segmentation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908551182548992 |
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| author | Schwaiger, Simon Thalhammer, Stefan Wöber, Wilfried Steinbauer-Wagner, Gerald |
| author_facet | Schwaiger, Simon Thalhammer, Stefan Wöber, Wilfried Steinbauer-Wagner, Gerald |
| contents | Understanding open-world semantics is critical for robotic planning and control, particularly in unstructured outdoor environments. Existing vision-language mapping approaches typically rely on object-centric segmentation priors, which often fail outdoors due to semantic ambiguities and indistinct class boundaries. We propose OTAS - an Open-vocabulary Token Alignment method for outdoor Segmentation. OTAS addresses the limitations of open-vocabulary segmentation models by extracting semantic structure directly from the output tokens of pre-trained vision models. By clustering semantically similar structures across single and multiple views and grounding them in language, OTAS reconstructs a geometrically consistent feature field that supports open-vocabulary segmentation queries. Our method operates in a zero-shot manner, without scene-specific fine-tuning, and achieves real-time performance of up to ~17 fps. On the Off-Road Freespace Detection dataset, OTAS yields a modest IoU improvement over fine-tuned and open-vocabulary 2D segmentation baselines. In 3D segmentation on TartanAir, it achieves up to a 151% relative IoU improvement compared to existing open-vocabulary mapping methods. Real-world reconstructions further demonstrate OTAS' applicability to robotic deployment. Code and a ROS 2 node are available at https://otas-segmentation.github.io/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_08851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OTAS: Open-vocabulary Token Alignment for Outdoor Segmentation Schwaiger, Simon Thalhammer, Stefan Wöber, Wilfried Steinbauer-Wagner, Gerald Robotics Understanding open-world semantics is critical for robotic planning and control, particularly in unstructured outdoor environments. Existing vision-language mapping approaches typically rely on object-centric segmentation priors, which often fail outdoors due to semantic ambiguities and indistinct class boundaries. We propose OTAS - an Open-vocabulary Token Alignment method for outdoor Segmentation. OTAS addresses the limitations of open-vocabulary segmentation models by extracting semantic structure directly from the output tokens of pre-trained vision models. By clustering semantically similar structures across single and multiple views and grounding them in language, OTAS reconstructs a geometrically consistent feature field that supports open-vocabulary segmentation queries. Our method operates in a zero-shot manner, without scene-specific fine-tuning, and achieves real-time performance of up to ~17 fps. On the Off-Road Freespace Detection dataset, OTAS yields a modest IoU improvement over fine-tuned and open-vocabulary 2D segmentation baselines. In 3D segmentation on TartanAir, it achieves up to a 151% relative IoU improvement compared to existing open-vocabulary mapping methods. Real-world reconstructions further demonstrate OTAS' applicability to robotic deployment. Code and a ROS 2 node are available at https://otas-segmentation.github.io/. |
| title | OTAS: Open-vocabulary Token Alignment for Outdoor Segmentation |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.08851 |