Summary of Query3d: Llm-powered Open-vocabulary Scene Segmentation with Language Embedded 3d Gaussian, by Amirhosein Chahe et al.
Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D Gaussian
by Amirhosein Chahe, Lifeng Zhou
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 novel method combines Language Embedded 3D Gaussians and Large Language Models (LLMs) to improve open-vocabulary 3D scene querying in autonomous driving. The approach leverages GPT-3.5 Turbo as an expert model to generate high-quality text data, which is then used to fine-tune smaller LLMs for on-device deployment. The method demonstrates significant performance improvements compared to traditional approaches based on predefined canonical phrases on the WayveScenes101 dataset. Ablation studies reveal that larger models are better equipped to leverage additional semantic information from helping positive words, achieving comparable performance while maintaining faster inference times. This work represents a significant advancement towards more efficient, context-aware autonomous driving systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes self-driving cars smarter by letting them understand 3D scenes better. It uses special language models to help the car’s computer quickly find important objects in its view. The new method is really good at finding things and works well even when the car’s computer is small and fast. This means that self-driving cars can make decisions faster and more accurately, which could help keep people safe on the road. |
Keywords
» Artificial intelligence » Gpt » Inference
