The accurate estimation of forest biomass is crucial for various applications, e.g., carbon stock assessment, forest management, and climate change mitigation. Terrestrial LiDAR scanners (TLS) have become increasingly popular for obtaining precise measurements of forest parameters at plot levels, including biomass. Obtaining biomass estimates from TLS data requires the use of leaf-filtering algorithms. However, the performance of available algorithms for various data and site conditions is unknown. Therefore, the aim of this study is to analyse the performance of different algorithms. Using 95 trees from five different site conditions (Cameroon, Guyana, Indonesia, Peru, and Germany), we compare four commonly used leaf-filtering algorithms: LeWoS, TLSeparation, CANUPO, and Random Forest (RF). We compare the algorithms in terms of computational efficiency, point-wise classification accuracy, and QSM-based volume extraction. We also investigate the influence of varying point cloud densities on achievable point cloud classification accuracy and the tree volume derived from the classified wood points. Our results show that the RF model outperforms other leaf-filtering algorithms in terms of pointwise classification accuracy, volume comparison, and resistance to point cloud density changes. TLSeparation had relatively the lowest point-wise classification accuracy, while LeWoS performed poorly in volume comparison and was most sensitive to variations in point cloud density. The performance of all algorithms decreased with decreasing point cloud density, demonstrating the importance of collecting data with a higher point cloud density. Furthermore, TLSeparation is the slowest in terms of processing time, while LeWoS is the fastest, followed by CANUPO and RF. All the investigated algorithms only use geometric features. It is expected that the additional use of radiometric features could further improve the accuracies. Our research gives an extensive overview of the investigated leaf-filtering algorithms for various site and data conditions and is an excellent decision-making aid in the selection of the appropriate algorithms.