OREN-X: Octree Residual Network for Real-Time Multi-Modal Mapping
This work addresses the high computational overhead and lack of cross-modal synergy in existing methods that independently represent geometric, radiometric, and vision-language information. We propose OREN-X, an online mapping framework that unifies multi-modal environment storage and retrieval via a shared octree data structure. A cross-modal synergy mechanism is designed to enhance signed distance function (SDF) estimation by leveraging occupancy and radiance fields. Furthermore, GPU-accelerated ray-tracing traversal combined with online dictionary learning for feature compression enables efficient storage and precise querying. The proposed method supports real-time mapping, achieving SDF computation exceeding 80 fps while improving near-surface SDF accuracy by 33%. Additionally, open-vocabulary 3D mIoU and average accuracy increase by 71% and 61%, respectively, significantly advancing autonomous navigation capabilities.