O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
<|reference_start|>O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation: Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit representation has provided a promising direction for online interactive mapping. However, implementing open-vocabulary scene understanding capability into online neural implicit mapping still faces three challenges: lack of local scene updating ability, blurry spatial hierarchical semantic segmentation and difficulty in maintaining multi-view consistency. To this end, we proposed O2V-mapping, which utilizes voxel-based language and geometric features to create an open-vocabulary field, thus allowing for local updates during online training process. Additionally, we leverage a foundational model for image segmentation to extract language features on object-level entities, achieving clear segmentation boundaries and hierarchical semantic features. For the purpose of preserving consistency in 3D object properties across different viewpoints, we propose a spatial adaptive voxel adjustment mechanism and a multi-view weight selection method. Extensive experiments on open-vocabulary object localization and semantic segmentation demonstrate that O2V-mapping achieves online construction of language scenes while enhancing accuracy, outperforming the previous SOTA method.<|reference_end|>
@article{tie2024o2v-mapping:,
    title={O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit
  Representation},
    author={Muer Tie, Julong Wei, Zhengjun Wang, Ke Wu, Shansuai Yuan, Kaizhao
  Zhang, Jie Jia, Jieru Zhao, Zhongxue Gan, Wenchao Ding},
    journal={arXiv preprint arXiv:2404.06836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06836},
    primaryClass={cs.CV}
}