Coded aperture snapshot spectral imaging (CASSI) is a promising technique for capturing three-dimensional hyperspectral images (HSIs), in which algorithms are used to perform the inverse problem of HSI reconstruction from a single coded two-dimensional (2D) measurement. Due to the ill-posed nature of this problem, various regularizers have been exploited to reconstruct 3D data from 2D measurements. Unfortunately, the accuracy and computational complexity are unsatisfactory. One feasible solution is to utilize additional information such as the RGB measurement in CASSI. Considering the combined CASSI and RGB measurements, in this paper, we propose a fusion model for HSI reconstruction. Specifically, we investigate the low-dimensional spectral subspace property of HSIs composed of a spectral basis and spatial coefficients. In particular, the RGB measurement is utilized to estimate the coefficients, while the CASSI measurement is adopted to provide the spectral basis. We further propose a patch processing strategy to enhance the spectral low-rank property of HSIs. The optimization of the proposed model requires neither iteration nor the spectral sensing matrix of the RGB detector. Extensive experiments on both simulated and real HSI datasets demonstrate that our proposed method not only outperforms previous state-of-the-art (iterative algorithms) methods in quality but also speeds up the reconstruction by more than 5000 times.