## 1 Introduction  
The management of slope hazards (e.g., rockfalls, landslides) along transportation corridors and in metropolitan settings poses a significant challenge to public officials. Slope hazards are not only costly, but they present a significant threat to life in many parts of the world [1]. Remote sensing technologies such as terrestrial laser scanning (TLS, also known as terrestrial lidar) show promise for the rapid assessment of unstable cliffs. Several studies have successfully utilized TLS for slope stability assessments and monitoring. [PERSON] _et al._[2] and [PERSON] \(\mathbf{\hat{a}}\)_et al._[3] provide a detailed review of applications, benefits and challenges of lidar for landslide and rock slope stability studies. [PERSON] and [PERSON] [4] evaluated the use of laser scanning for highway rock slope stability analysis and found that TLS offered several advantages compared with traditional techniques, including safety, accuracy, access, and analysis speed.  
Multi-temporal datasets acquired using TLS technology enable detailed change analyses through time [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. These repeated surveys enable engineers and scientists to understand the progressive patterns of failures and discern the influence of environmental conditions [21, 22, 23, 24, 25, 26, 27], which lead to those failures. Comparisons of each TLS survey enable the quantification of erosion rates and surface deformation, which can be used to analyze the pattern and propagation of displacements that have taken place over the monitoring period. For example, [PERSON] _et al._[28] demonstrate how rockfall hazards along transportation corridors can be monitored using mobile laser scanning (MLS) on both railway- and roadway-based systems. In both situations, MLS provided increased efficiency, safety, and the ability to investigate hazards compared with TLS.  
Researchers such as [17, 26, 29, 30, 31, 32] have then used these multi-temporal datasets in order to establish magnitude-frequency relationships to show the frequency distribution of large to small volumes of material release. Such distributions provide important information needed to support landslide hazard and risk assessment [31, 32]. [PERSON] and [PERSON] [33] present a novel approach for rockfall detection from repeat lidar surveys by generating clusters based on a K-nearest neighbors algorithm with clutter removal capabilities. The authors of [34] build on the process developed in [33] with a semi-automatic approach to calculate volumes of each rockfall cluster to generate magnitude-frequency relationships.  
To perform meaningful volumetric change analyses to develop these magnitude-frequency relationships, point clouds are commonly triangulated to produce continuous surface models. However, surface modeling methods can be difficult to employ on surfaces exhibiting high amounts of topographic variability and morphologic complexity. Further, the point sampling on the cliff surface is not uniformly distributed. Many modeling techniques (e.g., [35, 36, 37, 38, 39]) have been developed for artificial objects, which are generally smooth and transitions are well defined. Such approaches often do not translate well to creating topographic models where surface changes can vary significantly across small distances [40]. This, in turn, leads to topological artifacts. For accurate volumetric change analysis, surfaces must be free of intersecting triangles, have consistent facet normal orientations, and be free of data gaps (_i.e._, _holes_) in the areas to be analyzed. Processing procedures to generate such topologically correct models often require significant manual editing, which can be difficult and cumbersome. Moreover, while a variety of hole filling approaches exist, they are often dependent on the modeler's interpretation, and thus include an element of subjectivity. The influence of these techniques on the resulting volumetric analyses is often not considered or clearly documented.