OVI-MAP:Open-Vocabulary Instance-Semantic Mapping
This addresses the problem of enabling autonomous agents to map complex environments with flexible semantic understanding, though it appears incremental as it builds on existing mapping and vision-language methods.
The paper tackles the challenge of incremental open-vocabulary 3D instance-semantic mapping for autonomous agents by decoupling instance reconstruction from semantic inference, resulting in a system that operates in real-time and outperforms state-of-the-art baselines on standard benchmarks.
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.