Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at multiple wavelengths, theoretically offer distinct information for material identification, as variations in intensity of electromagnetic radiation reflected by a surface depend on the material composition of a scene. However, existing hyperspectral datasets are impoverished regarding the number of images and material categories for the dense material segmentation task, and collecting and annotating hyperspectral images with a spectral camera is prohibitively expensive. To address this, we propose a new model, the MatSpectNet to segment materials with recovered hyperspectral images from RGB images. The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets. The reconstructed hyperspectral images are further filtered using learned response curves and enhanced with human perception. The performance of MatSpectNet is evaluated on the LMD dataset as well as the OpenSurfaces dataset. Our experiments demonstrate that MatSpectNet attains a 1.60% increase in average pixel accuracy and a 3.42% improvement in mean class accuracy compared with the most recent publication. The project code is attached to the supplementary material and will be published on GitHub.

翻译：从三通道RGB图像中实现精确材料分割极具挑战性，因其外观存在显著差异。高光谱图像作为多个波长采样的光谱测量集，理论上可为材料识别提供独特信息——表面反射电磁辐射强度的变化取决于场景材料组成。然而，现有高光谱数据集在密集材料分割任务中面临图像数量不足与材料类别匮乏的困境，且使用光谱相机采集标注高光谱图像的成本过高。为此，我们提出新模型MatSpectNet，通过从RGB图像恢复的高光谱图像进行材料分割。该网络利用现代相机颜色感知原理约束重建的高光谱图像，并采用域适应方法将从光谱恢复数据集习得的高光谱重建能力泛化至材料分割数据集。重建的高光谱图像进一步通过学习响应曲线滤波，并结合人类视觉感知进行增强。在LMD数据集与OpenSurfaces数据集上的评估表明，相较于最新成果，MatSpectNet的平均像素精度提升1.60%，平均类别准确率提高3.42%。项目代码已附于补充材料中，并将发布在GitHub上。