Summary of Llmi3d: Mllm-based 3d Perception From a Single 2d Image, by Fan Yang et al.
LLMI3D: MLLM-based 3D Perception from a Single 2D Image
by Fan Yang, Sicheng Zhao, Yanhao Zhang, Hui Chen, Haonan Lu, Jungong Han, Guiguang Ding
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recent paper proposes innovative solutions to improve 3D perception algorithms for autonomous driving, augmented reality, robotics, and embodied intelligence. The authors identify the limitations of current approaches, including poor generalization in open scenarios and insufficient spatial feature extraction. To address these challenges, they introduce three novel techniques: Spatial-Enhanced Local Feature Mining for extracting better spatial features, 3D Query Token-Derived Info Decoding for precise geometric regression, and Geometry Projection-Based 3D Reasoning for handling camera focal length variations. The proposed LLMI3D model, a pre-trained multimodal large language model fine-tuned using parameter-efficient methods, outperforms existing approaches in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, scientists developed new ways to help machines understand the world better. They noticed that some computers have trouble recognizing shapes and objects when they are far away or moving quickly. To fix this problem, the researchers created three new ideas: one helps computers learn more about where things are, another helps them figure out precise measurements, and a third lets them adjust for different camera views. The team also made a special computer program called LLMI3D that can recognize 3D shapes better than other programs. They tested it and found that it works really well. |
Keywords
» Artificial intelligence » Feature extraction » Generalization » Large language model » Parameter efficient » Regression » Token
