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PanOVOcc establishes a training-free framework that integrates panoramic visual SLAM, open-vocabulary vision-language perception, and long-term spatial voxel memory into a unified online architecture. Addressing the limited fields-of-view of perspective sensors and the single-frame constraints of existing panoramic methods, the system continuously aggregates geometric and semantic observations into a globally queryable 3D occupancy map. Across two newly introduced sequential benchmarks, the approach substantially outperforms existing baselines, achieving absolute gains of up to +43.26 occupancy IoU and +20.16 semantic mIoU in real-world scenes.
Panoramic video streams can now be continuously fused into persistent, language-queryable 3D world models entirely zero-shot, driving a 43-point occupancy IoU leap over conventional mapping baselines.
Persistent semantic occupancy mapping is essential for embodied scene understanding. However, perspective-based systems provide limited spatial coverage, while existing panoramic methods primarily predict local volumes from single observations. We introduce PanOVOcc, a training-free framework for persistent open-vocabulary semantic occupancy mapping from panoramic sequences. PanOVOcc unifies panoramic SLAM, open-vocabulary perception, and long-term spatial voxel memory within an online architecture, continuously integrating geometric and semantic evidence into a global, language-queryable map. To facilitate systematic evaluation of this setting, we establish Pan-Replica and Pan-Holo360D, two benchmarks pairing continuous panoramic RGB-D sequences with scene-level semantic occupancy ground truth across synthetic and real-world scenes. Compared with the strongest evaluated baseline for each metric, PanOVOcc improves occupancy IoU and semantic mIoU by absolute +20.03 and +7.06 on Pan-Replica, and by +43.26 and +20.16 on Pan-Holo360D, respectively. The source code and the established benchmarks will be available at https://github.com/bakereet/PanOVOcc.