A point cloud is a set of data points in space. Point cloud registration is the process of aligning two or more 3D point clouds collected from different locations of the same scene. Registration enables point cloud data to be transformed into a common coordinate system, forming an integrated dataset representing the scene surveyed. In addition to those reliant on targets being placed in the scene before data capture, there are various registration methods available that are based on using only the point cloud data captured. Until recently, cloud-to-cloud registration methods have generally been centered upon the use of a coarse-to-fine optimization strategy. The challenges and limitations inherent in this process have shaped the development of point cloud registration and the associated software tools over the past three decades. Based on the success of deep learning methods applied to imagery data, attempts at applying these approaches to point cloud datasets have received much attention. This study reviews and comments on more recent developments in point cloud registration without using any targets and explores remaining issues, based on which recommendations on potential future studies in this topic are made.

The lateral earth pressure at rest is typically considered in situations where lateral wall movements are negligible. Determining the coefficient of lateral earth pressure at rest (referred to as K₀) often relies on established classical equations. However, these equations often overlook the influence of the width of the backfill soil on lateral earth pressure. While this omission is generally acceptable when the backfill soil is wide enough, there are instances where a retaining wall supports backfill soils of limited width, such as basement walls between adjacent buildings. Yet, there is limited research addressing the impact of narrow backfill in such scenarios. We aimed to address this gap by investigating variations in K₀ values under different conditions, including backfill width and soil properties. Using ABAQUS for numerical simulations, we refined and validated our model using relevant laboratory experimental data. Subsequently, the validated model was applied to various simulation scenarios. For narrow backfill widths (ranging from 0.1 to 0.7 times the retaining wall height), our findings indicated a general decrease in K₀ values with decreasing backfill widths, often smaller than those estimated using classical equations. Additionally, along the depth of the wall, K₀ values tended to decrease with increasing depth for narrow backfill widths. These findings contribute to our understanding of the impact of narrow backfill on K₀.

Terrain surface roughness, often described abstractly, poses challenges in quantitative characterization with various descriptors found in the literature. In this study, we compared five commonly used roughness descriptors, exploring correlations among their quantified terrain surface roughness maps across three terrains with distinct spatial variations. Additionally, we investigated the impacts of spatial scales and interpolation methods on these correlations. Dense point cloud data obtained through Light Detection and Ranging technique were used in this study. The findings highlighted both global pattern similarities and local pattern distinctions in the derived roughness maps, emphasizing the significance of incorporating multiple descriptors in studies where local roughness values play a crucial role in subsequent analyses. The spatial scales were found to have a smaller impact on rougher terrain, while interpolation methods had minimal influence on roughness maps derived from different descriptors.