Summary of Any2point: Empowering Any-modality Large Models For Efficient 3d Understanding, by Yiwen Tang et al.
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
by Yiwen Tang, Ray Zhang, Jiaming Liu, Zoey Guo, Dong Wang, Zhigang Wang, Bin Zhao, Shanghang Zhang, Peng Gao, Hongsheng Li, Xuelong Li
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces Any2Point, a novel approach to empower large foundation models for 3D understanding. The method is parameter-efficient and can be applied to any modality (vision, language, audio) by leveraging pre-trained transformers. A 3D-to-any virtual projection strategy is proposed, which correlates input 3D points with original 1D or 2D positions within the source modality. This enables positional encodings paired with the pre-trained model, avoiding geometry loss and promoting 3D learning with 1D/2D priors. The adapter module guides local feature aggregation of 3D tokens, adapting transformers to any modality. Experimental results demonstrate the effectiveness and efficiency of Any2Point. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help big language models understand 3D objects. It’s like a special key that unlocks the model’s ability to learn about 3D things from 1D or 2D data. The approach is efficient and can be used with different types of input (vision, language, audio). The team created a special projection method that connects 3D points to their original positions in 1D or 2D space. This helps the model learn about 3D objects without losing important spatial information. |
Keywords
* Artificial intelligence * Parameter efficient
