Summary of Thermalgaussian: Thermal 3d Gaussian Splatting, by Rongfeng Lu et al.
ThermalGaussian: Thermal 3D Gaussian Splatting
by Rongfeng Lu, Hangyu Chen, Zunjie Zhu, Yuhang Qin, Ming Lu, Le Zhang, Chenggang Yan, Anke Xue
First submitted to arxiv on: 11 Sep 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 paper proposes a novel approach to reconstructing thermal scenes in 3D from a set of thermal and RGB images using Neural Radiance Fields (NeRF) and 3D Gaussian splatting (3DGS). The ThermalGaussian method is designed for real-time rendering and rapid training, making it suitable for military surveillance applications. The authors first calibrate the RGB and thermal cameras to ensure accurate alignment, then learn multimodal 3D Gaussians using registered images. To prevent overfitting, multimodal regularization constraints are introduced, along with smoothing constraints tailored to the physical characteristics of the thermal modality. A real-world dataset named RGBT-Scenes is contributed, allowing for future research on thermal scene reconstruction. Experimental results show that ThermalGaussian achieves photorealistic rendering of thermal images and improves RGB image quality, while reducing model storage costs by 90%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Thermal imaging helps the military with surveillance cameras. Researchers are working on ways to reconstruct thermal scenes in 3D from a set of images. They’re using Neural Radiance Fields (NeRF) and a method called 3D Gaussian splatting (3DGS). This new approach is fast and can be used in real-time, which is helpful for the military. The team first makes sure the cameras are aligned correctly, then uses the images to learn how to reconstruct thermal scenes. To avoid mistakes, they added special rules to help the computer learn. They also created a dataset of real-world images that others can use to improve their own work. |
Keywords
» Artificial intelligence » Alignment » Overfitting » Regularization
