Accurate and spatially dense 3D displacement estimation can contribute to a better understanding of geomorphological processes, while long-range terrestrial laser scanning (LR-TLS) has emerged as a promising technique for generating such observations. However, selecting the most effective algorithms for dense 3D displacement estimation remains challenging due to the lack of benchmarking. This study introduces an open and extensible benchmarking framework for 3D displacement estimation and provides an initial validation through a systematic comparison of representative 2D projection-based and 3D point cloud–based methods for estimating 3D displacements from LR-TLS scans. The evaluation includes 252 combinations of algorithmic and hyperparameter configurations, covering cross-correlation, optical flow, and salient feature tracking approaches, as well as the 3D displacement estimation method F2S3. All methods were benchmarked on a single common LR-TLS dataset, using sparse GNSS and manually derived displacements as ground truth. Results show that F2S3 achieves the highest agreement with the ground truth, while the top-performing configurations of the 2D approaches reach comparable accuracy, albeit slightly lower than that of F2S3. Our findings further highlight key sensitivities of current methods to parameter choices and data characteristics. The presented open and extensible evaluation framework enables reproducible performance assessment and could provide a foundation for future large-scale benchmarking and further development of 3D displacement estimation techniques for LR-TLS data.