Summary of Fmgs: Foundation Model Embedded 3d Gaussian Splatting For Holistic 3d Scene Understanding, by Xingxing Zuo et al.
FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding
by Xingxing Zuo, Pouya Samangouei, Yunwen Zhou, Yan Di, Mingyang Li
First submitted to arxiv on: 3 Jan 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 The authors introduce Foundation Model Embedded Gaussian Splatting (FMGS), a novel method that combines 3D Gaussian Splatting with vision-language embeddings from foundation models. This approach enables efficient reconstruction and representation of 3D vision-language models, which is crucial for augmented reality and robotic applications. The key innovation lies in distilling feature maps generated from image-based foundation models into those rendered from the 3D model. To achieve this, the authors introduce a novel scene representation that integrates strengths from both Gaussian Splatting and multi-resolution hash encodings. The training procedure also incorporates a pixel alignment loss to ensure high-quality rendering and fast inference. Experimental results demonstrate remarkable multi-view semantic consistency, outperforming state-of-the-art methods by 10.2 percent on open-vocabulary language-based object detection while being 851X faster for inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to understand 3D objects using computer vision and natural language processing. The authors create a model that can take in both images and text about an object, and then use that information to generate a detailed 3D description of the object. This is useful because it allows computers to better understand the world around them, which has many applications in fields like augmented reality and robotics. The authors’ approach is faster and more accurate than previous methods, making it a big step forward in this area. |
Keywords
* Artificial intelligence * Alignment * Inference * Natural language processing * Object detection
