Summary of Spectralgaussians: Semantic, Spectral 3d Gaussian Splatting For Multi-spectral Scene Representation, Visualization and Analysis, by Saptarshi Neil Sinha and Holger Graf and Michael Weinmann
SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis
by Saptarshi Neil Sinha, Holger Graf, Michael Weinmann
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 proposed novel cross-spectral rendering framework, 3D Gaussian Splatting (3DGS), generates realistic and semantically meaningful splats from registered multi-view spectrum and segmentation maps. This extension enhances the representation of scenes with multiple spectra, providing insights into underlying materials and segmentation. The approach introduces an improved physically-based rendering method for Gaussian splats, estimating reflectance and lights per spectrum, enhancing accuracy and realism. The framework is evaluated against recent learning-based spectral scene representation approaches (XNeRF and SpectralNeRF) and non-spectral state-of-the-art learning-based approaches, demonstrating superior performance. The work also demonstrates the potential of spectral scene understanding for precise scene editing techniques like style transfer, inpainting, and removal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to represent scenes with multiple types of data. It uses something called 3D Gaussian Splatting (3DGS) to make this representation. This helps us understand the materials and objects in the scene better. The approach is tested against other methods and shown to be more accurate and realistic. The results can be used for things like changing the style of a picture, filling in missing parts, or removing unwanted objects. |
Keywords
» Artificial intelligence » Scene understanding » Style transfer
