# OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations Source: [OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:57:13.861Z Authors: Christina Kassab, Sacha Morin, Martin B\\"uchner, Mat\\'ias Mattamala, Kumaraditya Gupta, Abhinav Valada, Liam Paull, Maurice Fallon Published: 2025-03-25T15:28:50.000Z ## Abstract 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/. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()