LOSC: LiDAR Open-voc Segmentation Consolidator
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866910052164567040 |
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| author | Samet, Nermin Puy, Gilles Marlet, Renaud |
| author_facet | Samet, Nermin Puy, Gilles Marlet, Renaud |
| contents | We study the use of image-based Vision-Language Models (VLMs) for open-vocabulary segmentation of lidar scans in driving settings. Classically, image semantics can be back-projected onto 3D point clouds. Yet, resulting point labels are noisy and sparse. We consolidate these labels to enforce both spatio-temporal consistency and robustness to image-level augmentations. We then train a 3D network based on these refined labels. This simple method, called LOSC, outperforms the SOTA of zero-shot open-vocabulary semantic and panoptic segmentation on both nuScenes and SemanticKITTI, with significant margins. Code is available at https://github.com/valeoai/LOSC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LOSC: LiDAR Open-voc Segmentation Consolidator Samet, Nermin Puy, Gilles Marlet, Renaud Computer Vision and Pattern Recognition We study the use of image-based Vision-Language Models (VLMs) for open-vocabulary segmentation of lidar scans in driving settings. Classically, image semantics can be back-projected onto 3D point clouds. Yet, resulting point labels are noisy and sparse. We consolidate these labels to enforce both spatio-temporal consistency and robustness to image-level augmentations. We then train a 3D network based on these refined labels. This simple method, called LOSC, outperforms the SOTA of zero-shot open-vocabulary semantic and panoptic segmentation on both nuScenes and SemanticKITTI, with significant margins. Code is available at https://github.com/valeoai/LOSC. |
| title | LOSC: LiDAR Open-voc Segmentation Consolidator |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.07605 |