OVI-MAP:Open-Vocabulary Instance-Semantic Mapping
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917364543520768 |
|---|---|
| author | Deng, Zilong Tombari, Federico Pollefeys, Marc Wald, Johanna Barath, Daniel |
| author_facet | Deng, Zilong Tombari, Federico Pollefeys, Marc Wald, Johanna Barath, Daniel |
| contents | Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the need for robust instance segmentation, real-time processing, and flexible open-set reasoning. Existing methods often rely on the closed-set assumption or dense per-pixel language fusion, which limits scalability and temporal consistency. We introduce OVI-MAP that decouples instance reconstruction from semantic inference. We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input, while semantic features are extracted only from a small set of automatically selected views using vision-language models. This design enables stable instance tracking and zero-shot semantic labeling throughout online exploration. Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26541 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OVI-MAP:Open-Vocabulary Instance-Semantic Mapping Deng, Zilong Tombari, Federico Pollefeys, Marc Wald, Johanna Barath, Daniel Computer Vision and Pattern Recognition Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the need for robust instance segmentation, real-time processing, and flexible open-set reasoning. Existing methods often rely on the closed-set assumption or dense per-pixel language fusion, which limits scalability and temporal consistency. We introduce OVI-MAP that decouples instance reconstruction from semantic inference. We propose to build a class-agnostic 3D instance map that is incrementally constructed from RGB-D input, while semantic features are extracted only from a small set of automatically selected views using vision-language models. This design enables stable instance tracking and zero-shot semantic labeling throughout online exploration. Our system operates in real time and outperforms state-of-the-art open-vocabulary mapping baselines on standard benchmarks. |
| title | OVI-MAP:Open-Vocabulary Instance-Semantic Mapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.26541 |