OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
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arXiv
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| Format: | Preprint |
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2025
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| author | Kassab, Christina Morin, Sacha Büchner, Martin Mattamala, Matías Gupta, Kumaraditya Valada, Abhinav Paull, Liam Fallon, Maurice |
| author_facet | Kassab, Christina Morin, Sacha Büchner, Martin Mattamala, Matías Gupta, Kumaraditya Valada, Abhinav Paull, Liam Fallon, Maurice |
| contents | 3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at present the evaluation of these representations is limited to datasets with closed-set semantics that do not capture the richness of language. This work presents OpenLex3D, a dedicated benchmark for evaluating 3D open-vocabulary scene representations. OpenLex3D provides entirely new label annotations for scenes from Replica, ScanNet++, and HM3D, which capture real-world linguistic variability by introducing synonymical object categories and additional nuanced descriptions. Our label sets provide 13 times more labels per scene than the original datasets. By introducing an open-set 3D semantic segmentation task and an object retrieval task, we evaluate various existing 3D open-vocabulary methods on OpenLex3D, showcasing failure cases, and avenues for improvement. Our experiments provide insights on feature precision, segmentation, and downstream capabilities. The benchmark is publicly available at: https://openlex3d.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations Kassab, Christina Morin, Sacha Büchner, Martin Mattamala, Matías Gupta, Kumaraditya Valada, Abhinav Paull, Liam Fallon, Maurice Computer Vision and Pattern Recognition Robotics 3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at present the evaluation of these representations is limited to datasets with closed-set semantics that do not capture the richness of language. This work presents OpenLex3D, a dedicated benchmark for evaluating 3D open-vocabulary scene representations. OpenLex3D provides entirely new label annotations for scenes from Replica, ScanNet++, and HM3D, which capture real-world linguistic variability by introducing synonymical object categories and additional nuanced descriptions. Our label sets provide 13 times more labels per scene than the original datasets. By introducing an open-set 3D semantic segmentation task and an object retrieval task, we evaluate various existing 3D open-vocabulary methods on OpenLex3D, showcasing failure cases, and avenues for improvement. Our experiments provide insights on feature precision, segmentation, and downstream capabilities. The benchmark is publicly available at: https://openlex3d.github.io/. |
| title | OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2503.19764 |