@misc{deng2026ovi-map,
author = {Deng, Zilong and Tombari, Federico and Pollefeys, Marc and Wald, Johanna and Baráth, Dániel},
title = {OVI-MAP:Open-Vocabulary Instance-Semantic Mapping},
howpublished = {arXiv},
year = {2026},
month = {March},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/ovi-mapopen-vocabulary-instance-semantic-mapping/},
}