Adversarial attack on point clouds plays a vital role in evaluating and improving the
adversarial robustness of 3D deep learning models. Current attack methods are mainly …

Point cloud compression (PCC) is a key enabler for various 3-D applications, owing to the
universality of the point cloud format. Ideally, 3D point clouds endeavor to depict …

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-
to-sample methods have recently received increasing attention from the community for …

Point clouds are characterized by irregularity and unstructuredness, which pose challenges
in efficient data exploitation and discriminative feature extraction. In this paper, we present …

Previous research in multi-view 3D reconstruction had used different convolution neural
network (CNN) architectures to obtain a 3D voxel representation. Even though CNN works …

In this article, we propose a new distortion quantification method for point clouds, the
multiscale potential energy discrepancy (MPED). Currently, there is a lack of effective …

Many problems in machine learning involve data sets in which each data point is a point
cloud in $\mathbb {R}^ D $. A growing number of applications require a means of measuring …

Autonomous assembly of objects is an essential task in robotics and 3D computer vision. It
has been studied extensively in robotics as a problem of motion planning, actuator control …

Explicit neural surface representations allow for exact and efficient extraction of the encoded
surface at arbitrary precision, as well as analytic derivation of differential geometric …

Generating dense point clouds from sparse raw data benefits downstream 3D
understanding tasks, but existing models are limited to a fixed upsampling ratio or to a short …