Search Results for author:
Found 5 papers, 4 papers with code
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers.
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
To this end, we introduce Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
Ranked #5 on
3D Question Answering (3D-QA)
on 3D MM-Vet
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
