1 code implementation • ECCV 2020 • Panos Achlioptas, Ahmed Abdelreheem, Fei Xia, Mohamed Elhoseiny, Leonidas Guibas
Due to the scarcity and unsuitability of existent 3D-oriented linguistic resources for this task, we first develop two large-scale and complementary visio-linguistic datasets: i) extbf{ extit{Sr3D}}, which contains 83. 5K template-based utterances leveraging extit{spatial relations} with other fine-grained object classes to localize a referred object in a given scene, and ii) extbf{ extit{Nr3D}} which contains 41. 5K extit{natural, free-form}, utterances collected by deploying a 2-player object reference game in 3D scenes.
1 code implementation • 27 Oct 2023 • Habib Slim, Xiang Li, Yuchen Li, Mahmoud Ahmed, Mohamed Ayman, Ujjwal Upadhyay, Ahmed Abdelreheem, Arpit Prajapati, Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny
In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks.
no code implementations • 5 Jun 2023 • Ahmed Abdelreheem, Abdelrahman Eldesokey, Maks Ovsjanikov, Peter Wonka
Instead, we propose to exploit the in-context learning capabilities of ChatGPT to generate two different sets of semantic regions for each shape and a semantic mapping between them.
no code implementations • ICCV 2023 • Ahmed Abdelreheem, Ivan Skorokhodov, Maks Ovsjanikov, Peter Wonka
We explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models.
no code implementations • 12 Dec 2022 • Ahmed Abdelreheem, Kyle Olszewski, Hsin-Ying Lee, Peter Wonka, Panos Achlioptas
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data.