Summary of Evaluating Modern Approaches in 3d Scene Reconstruction: Nerf Vs Gaussian-based Methods, by Yiming Zhou and Zixuan Zeng and Andi Chen and Xiaofan Zhou and Haowei Ni and Shiyao Zhang and Panfeng Li and Liangxi Liu and Mengyao Zheng and Xupeng Chen
Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods
by Yiming Zhou, Zixuan Zeng, Andi Chen, Xiaofan Zhou, Haowei Ni, Shiyao Zhang, Panfeng Li, Liangxi Liu, Mengyao Zheng, Xupeng Chen
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper investigates the capabilities of Neural Radiance Fields (NeRF) and Gaussian-based methods in 3D scene reconstruction, comparing them to traditional Simultaneous Localization and Mapping (SLAM) systems. The authors use datasets like Replica and ScanNet to evaluate performance based on tracking accuracy, mapping fidelity, and view synthesis. Results show that NeRF excels in view synthesis, generating new perspectives from existing data at slower processing speeds. In contrast, Gaussian-based methods provide rapid processing and expressiveness but lack comprehensive scene completion. Newer methods like NICE-SLAM and SplaTAM demonstrate superior performance in dynamic environments, outperforming older frameworks like ORB-SLAM2. This comparative analysis bridges theoretical research with practical implications, shedding light on future developments in robust 3D scene reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different ways to reconstruct 3D scenes from photos and videos. It looks at two new methods, Neural Radiance Fields (NeRF) and Gaussian-based methods, and compares them to traditional techniques called Simultaneous Localization and Mapping (SLAM). The researchers use special datasets to test how well each method works in different situations. They found that NeRF is really good at creating new views of the same scene from just a few photos or videos. However, it takes longer to process this information. The other method is faster but not as good at reconstructing the whole scene. This study helps us understand which methods are best for different tasks and can be used in many real-world applications. |
Keywords
* Artificial intelligence * Tracking
