Laser point cloud filtering is a fundamental step in various applications of light detection and ranging (LiDAR) data. The progressive triangulated irregular network (TIN) densification (PTD) filtering algorithm is a classic method and is widely used due to its robustness and effectiveness. However, the performance of the PTD filtering algorithm depends on the quality of the initial TIN-based digital terrain model (DTM). The filtering effect is also limited by the tuning of a number of parameters to cope with various terrains. Therefore, an improved PTD filtering algorithm based on a multiscale cylindrical neighborhood (PTD-MSCN) is proposed and implemented to enhance the filtering effect in complex terrains. In the PTD-MSCN algorithm, the multiscale cylindrical neighborhood is used to obtain and densify ground seed points to create a high-quality DTM. By linearly decreasing the radius of the cylindrical neighborhood and the distance threshold, the PTD-MSCN algorithm iteratively finds ground seed points and removes object points. To evaluate the performance of the proposed PTD-MSCN algorithm, it was applied to 15 benchmark LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission. The experimental results indicated that the average total error can be decreased from 5.31% when using the same parameter set to 3.32% when optimized. Compared with five other publicized PTD filtering algorithms, the proposed PTD-MSCN algorithm is not only superior in accuracy but also more robust.