Multi-Spectral Gaussian Splatting with Neural Color Representation

• URL: http://arxiv.org/abs/2506.03407v1
• Date: Tue, 03 Jun 2025 21:36:50 GMT
• Title: Multi-Spectral Gaussian Splatting with Neural Color Representation
• Authors: Lukas Meyer, Josef Grün, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, Linus Franke,
• Abstract summary: We present MS-Splatting -- a multi-spectral 3D Gaussian Splatting framework.<n>Our method does not require cross-modal camera calibration.<n>It is versatile enough to model a variety of different spectra, including thermal and near-infra red.
• Score: 8.200719250787651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present MS-Splatting -- a multi-spectral 3D Gaussian Splatting (3DGS) framework that is able to generate multi-view consistent novel views from images of multiple, independent cameras with different spectral domains. In contrast to previous approaches, our method does not require cross-modal camera calibration and is versatile enough to model a variety of different spectra, including thermal and near-infra red, without any algorithmic changes. Unlike existing 3DGS-based frameworks that treat each modality separately (by optimizing per-channel spherical harmonics) and therefore fail to exploit the underlying spectral and spatial correlations, our method leverages a novel neural color representation that encodes multi-spectral information into a learned, compact, per-splat feature embedding. A shallow multi-layer perceptron (MLP) then decodes this embedding to obtain spectral color values, enabling joint learning of all bands within a unified representation. Our experiments show that this simple yet effective strategy is able to improve multi-spectral rendering quality, while also leading to improved per-spectra rendering quality over state-of-the-art methods. We demonstrate the effectiveness of this new technique in agricultural applications to render vegetation indices, such as normalized difference vegetation index (NDVI).

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