Language Embedded 3D Gaussians for Open-Vocabulary Scene Querying
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Summary
This summary is machine-generated.We introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary querying. This method significantly improves visual quality and language query accuracy while maintaining real-time rendering speeds.
Area Of Science
- Computer Vision
- 3D Scene Understanding
- Natural Language Processing
Background
- Open-vocabulary querying in 3D is crucial for scene understanding but faces challenges with resource-intensive neural networks.
- Existing language-embedded 3D representations struggle with memory usage and performance when using 3D Gaussians.
- High-dimensional semantic features on 3D Gaussians lead to prohibitive memory costs and decreased efficacy.
Purpose Of The Study
- To develop a novel scene representation for efficient and accurate open-vocabulary querying in 3D.
- To address the memory and performance limitations of current language-embedded 3D representations.
- To enable high-quality visual querying and scene understanding in 3D environments.
Main Methods
- Introduced Language Embedded 3D Gaussians, a new scene representation.
- Proposed a quantization scheme to significantly reduce memory requirements.
- Developed a novel embedding procedure for smoother, high-accuracy querying and to counter multi-view inconsistencies.
Main Results
- Achieved superior visual quality and language querying accuracy compared to existing methods.
- Demonstrated real-time rendering frame rates on a single desktop GPU.
- Successfully alleviated memory requirements through a dedicated quantization scheme.
Conclusions
- Language Embedded 3D Gaussians offer an efficient and effective solution for open-vocabulary 3D scene querying.
- The proposed method balances high accuracy with real-time performance and reduced memory footprint.
- This representation advances 3D scene understanding and object localization tasks.
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