SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis

• URL: http://arxiv.org/abs/2408.06975v1
• Date: Tue, 13 Aug 2024 15:32:54 GMT
• Title: SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis
• Authors: Saptarshi Neil Sinha, Holger Graf, Michael Weinmann,
• Abstract summary: We propose a novel cross-spectral rendering framework based on 3D Gaussian Splatting (3DGS) This framework generates realistic and semantically meaningful splats from registered multi-view spectrum and segmentation maps. Our contributions address challenges in multi-spectral scene representation, rendering, and editing, offering new possibilities for diverse applications.
• Score: 3.8834382997684087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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