OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations

• URL: http://arxiv.org/abs/2503.19764v2
• Date: Tue, 14 Oct 2025 13:14:38 GMT
• Title: OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
• Authors: Christina Kassab, Sacha Morin, Martin Büchner, Matías Mattamala, Kumaraditya Gupta, Abhinav Valada, Liam Paull, Maurice Fallon,
• Abstract summary: 3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language.<n>This work presents OpenLex3D, a benchmark for evaluating 3D open-vocabulary scene representations.
• Score: 22.009991894527293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/.

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