RelationField: Relate Anything in Radiance Fields
| Autor: | Koch, Sebastian, Wald, Johanna, Colosi, Mirco, Vaskevicius, Narunas, Hermosilla, Pedro, Tombari, Federico, Ropinski, Timo |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | Neural radiance fields are an emerging 3D scene representation and recently even been extended to learn features for scene understanding by distilling open-vocabulary features from vision-language models. However, current method primarily focus on object-centric representations, supporting object segmentation or detection, while understanding semantic relationships between objects remains largely unexplored. To address this gap, we propose RelationField, the first method to extract inter-object relationships directly from neural radiance fields. RelationField represents relationships between objects as pairs of rays within a neural radiance field, effectively extending its formulation to include implicit relationship queries. To teach RelationField complex, open-vocabulary relationships, relationship knowledge is distilled from multi-modal LLMs. To evaluate RelationField, we solve open-vocabulary 3D scene graph generation tasks and relationship-guided instance segmentation, achieving state-of-the-art performance in both tasks. See the project website at https://relationfield.github.io. Comment: Project page: https://relationfield.github.io |
| Databáze: | arXiv |
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