Ayca Takmaz — accepted AI-conference papers

2026

Search3D: Hierarchical Open-Vocabulary 3D Segmentation

Ayca Takmaz, Alexandros Delitzas, Robert W. Sumner, Francis Engelmann, Johanna Wald, Federico Tombari

ICRA 2026poster

Open-vocabulary 3D segmentation enables the exploration of 3D spaces using free-form text descriptions. Existing methods for open-vocabulary 3D instance segmentation primarily focus on identifying object-level instances in a scene. However, they face challenges when it comes to understanding more fi…

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes

Alexandros Delitzas, Ayca Takmaz, Federico Tombari, Robert Sumner, Marc Pollefeys, Francis Engelmann

CVPR 2024poster

Existing 3D scene understanding methods are heavily focused on 3D semantic and instance segmentation. However identifying objects and their parts only constitutes an intermediate step towards a more fine-grained goal which is effectively interacting with the functional interactive elements (e.g. han…

Cited by 35 Source ↗PDF ↗Scholar ↗

2024

Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels

Rui Huang, Songyou Peng, Ayca Takmaz, Federico Tombari, Marc Pollefeys, Shiji Song +2

ECCV 2024poster

"Current 3D scene segmentation methods are heavily dependent on manually annotated 3D training datasets. Such manual annotations are labor-intensive, and often lack fine-grained details. Furthermore, models trained on this data typically struggle to recognize object classes beyond the annotated trai…

Cited by 34 Source ↗PDF ↗Scholar ↗