NeurIPS Conference 2025 Conference Paper
OpenLex3D: A Tiered Benchmark for Open-Vocabulary 3D Scene Representations
- Christina Kassab
- Sacha Morin
- Martin Büchner
- Matias Mattamala
- Kumaraditya Gupta
- Abhinav Valada
- Liam Paull
- Maurice Fallon
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/.