Summary of More Text, Less Point: Towards 3d Data-efficient Point-language Understanding, by Yuan Tang et al.
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
by Yuan Tang, Xu Han, Xianzhi Li, Qiao Yu, Jinfeng Xu, Yixue Hao, Long Hu, Min Chen
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The paper proposes a new task, 3D Data-Efficient Point-Language Understanding, which aims to enable Large Language Models (LLMs) to comprehend the 3D physical world with minimal 3D point cloud and text data pairs. To address this challenge, the authors introduce GreenPLM, a pre-trained point cloud-text encoder that maps the 3D point cloud space to the text space, allowing seamless connection to LLMs. The paper also introduces a three-stage training strategy and a zero-parameter cross-attention module for token pooling. Experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping computers understand the world in three dimensions, like we do when looking at objects around us. Currently, computers are not very good at this because they don’t have enough training data. The authors suggest a new way of training computers using text and 3D point cloud data to improve their understanding. They propose a method called GreenPLM that uses a special kind of mapping to connect the computer’s understanding of text with its understanding of 3D objects. This allows the computer to learn more quickly and accurately about 3D objects from just a small amount of training data. |
Keywords
» Artificial intelligence » Cross attention » Encoder » Language understanding » Token
