OpenLex3D: オープンボキャブラリ3Dシーン表現のための階層的評価ベンチマーク
OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
Replica、ScanNet++、HM3Dのシーンに同義語や詳細記述を含む新規ラベルを付与し、オープンボキャブラリ3Dシーン表現を評価するベンチマークを提案。既存手法の失敗事例や改善点を明らかにする。
著者: Christina Kassab, Sacha Morin, Martin Büchner, Matías Mattamala, Kumaraditya Gupta, Abhinav Valada, Liam Paull, Maurice Fallon
分類: cs.CV, cs.RO
原文アブストラクト
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/.
