Single-cell Hi-C (scHi-C) technology is widely used to measure individual cells’ three-dimensional genome structures and investigate cell-to-cell heterogeneity of multi-scale chromatin structures and cellular functions. It facilitates the identification of rare cell types and enhances the understanding of disease mechanisms. However, the sparsity of scHi-C data poses significant challenges for downstream analyses, such as cell clustering. Several scHi-C imputation methods have been proposed in recent years, including statistics-based and deep learning based methods. Nevertheless, these methods have not been comprehensively evaluated and analyzed in previous studies to the best of our knowledge. In this paper, seven state-of-the-art imputation methods are assessed and compared in terms of various metrics based on nine simulated datasets and one real dataset. Specifically, the performance of these methods in data recovery and cell clustering is evaluated. Experimental results show that deep learning based methods achieve better performance than statistics-based methods, but no method performs the best in all cases. Finally, we provide method recommendations for different scenarios.