  Precise 3D environmental mapping is pivotal in robotics. Existing methods
often rely on predefined concepts during training or are time-intensive when
generating semantic maps. This paper presents Open-Fusion, a groundbreaking
approach for real-time open-vocabulary 3D mapping and queryable scene
representation using RGB-D data. Open-Fusion harnesses the power of a
pre-trained vision-language foundation model (VLFM) for open-set semantic
comprehension and employs the Truncated Signed Distance Function (TSDF) for
swift 3D scene reconstruction. By leveraging the VLFM, we extract region-based
embeddings and their associated confidence maps. These are then integrated with
3D knowledge from TSDF using an enhanced Hungarian-based feature-matching
mechanism. Notably, Open-Fusion delivers outstanding annotation-free 3D
segmentation for open-vocabulary without necessitating additional 3D training.
Benchmark tests on the ScanNet dataset against leading zero-shot methods
highlight Open-Fusion's superiority. Furthermore, it seamlessly combines the
strengths of region-based VLFM and TSDF, facilitating real-time 3D scene
comprehension that includes object concepts and open-world semantics. We
encourage the readers to view the demos on our project page:
https://uark-aicv.github.io/OpenFusion
