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 …

Point cloud filtering is a fundamental problem in geometry modeling and processing.
Despite of significant advancement in recent years, the existing methods still suffer from two …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of point cloud denoising techniques. In this article, we first provide a …

We present an anisotropic point cloud denoising method using L 0 minimization. The L 0
norm directly measures the sparsity of a solution, and we observe that many common …

Feature lines are important geometric cues in characterizing the structure of a CAD model.
Despite great progress in both explicit reconstruction and implicit reconstruction, it remains a …

Point clouds acquired with LiDAR are widely adopted in various fields, such as three-
dimensional (3D) reconstruction, autonomous driving, and robotics. However, the high …

With better and faster acquisition devices comes a demand for fast robust reconstruction
algorithms, but no L1-based technique has been fast enough for online use so far. In this …

The advancement of 3D scanning technology has gradually brought to light the issue of
complex and time-consuming processing of high-density point cloud data. To address this …

Point cloud is the primary source from 3D scanners and depth cameras. It usually contains
more raw geometric features, as well as higher levels of noise than the reconstructed mesh …

Point set filtering, which aims at reconstructing noise-free point sets from their corresponding
noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of …