In recent years, 3D point cloud has gained increasing attention as a new representation for
objects. However, the raw point cloud is often noisy and contains outliers. Therefore, it is …

Field investigations of geometric discontinuity properties in rock masses are increasingly
using three-dimensional point cloud data. These point clouds sample the rock mass surface …

Persistent cracking and progressive damage can weaken the operational performance of
structures such as bridges, dams, and concrete buildings. Consequently, research into …

In this study, we explore the challenge of efficiently representing scenes with a constrained
number of Gaussians. Our analysis shifts from traditional graphics and 2D computer vision to …

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D
shape reconstruction and completion from a variety of 3D inputs, which are deficient in some …

Point set is a flexible and lightweight representation widely used for 3D deep learning.
However, their discrete nature prevents them from representing continuous and fine …

Recent advancement of remote sensing technologies has brought in accurate, dense, and
inexpensive city-scale Light Detection And Ranging (LiDAR) point clouds, which can be …

In this paper we present a novel on-line method to recursively align point clouds. By
considering each point together with the local features of the surface (normal and curvature) …

Processing large point clouds is a challenging task. Therefore, the data is often sampled to a
size that can be processed more easily. The question is how to sample the data? A popular …

The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process
of accurate registration of 3D point cloud data. The algorithm requires a proper initial value …