SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis
<|reference_start|>SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis: We propose a novel cross-spectral rendering framework based on 3D Gaussian Splatting (3DGS) that 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 the underlying materials and segmentation. We introduce an improved physically-based rendering approach for Gaussian splats, estimating reflectance and lights per spectra, thereby enhancing accuracy and realism. In a comprehensive quantitative and qualitative evaluation, we demonstrate the superior performance of our approach with respect to other recent learning-based spectral scene representation approaches (i.e., XNeRF and SpectralNeRF) as well as other non-spectral state-of-the-art learning-based approaches. Our work also demonstrates the potential of spectral scene understanding for precise scene editing techniques like style transfer, inpainting, and removal. Thereby, our contributions address challenges in multi-spectral scene representation, rendering, and editing, offering new possibilities for diverse applications.<|reference_end|>
@article{sinha2024spectralgaussians:,
    title={SpectralGaussians: Semantic, spectral 3D Gaussian splatting for
  multi-spectral scene representation, visualization and analysis},
    author={Saptarshi Neil Sinha and Holger Graf and Michael Weinmann},
    journal={arXiv preprint arXiv:2408.06975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06975},
    primaryClass={cs.CV cs.AI cs.GR}
}