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Found 5 papers, 2 papers with code
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
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.
3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition
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.
Zero-Shot 3D Shape Correspondence
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.
SATR: Zero-Shot Semantic Segmentation of 3D Shapes
We explore the task of zero-shot semantic segmentation of 3D shapes by using large-scale off-the-shelf 2D image recognition models.
ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data.
