Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

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 …

The ability to create realistic animatable and relightable head avatars from casual video
sequences would open up wide ranging applications in communication and entertainment …

Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This
paper presents a new point cloud upsampling network called PU-GAN, which is formulated …

Learning and analyzing 3D point clouds with deep networks is challenging due to the
sparseness and irregularity of the data. In this paper, we present a data-driven point cloud …

In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from
an input point cloud. Instead of explicitly specifying a prior that encodes the expected shape …

We present a detail-driven deep neural network for point set upsampling. A high-resolution
point set is essential for point-based rendering and surface reconstruction. Inspired by the …

The area of surface reconstruction has seen substantial progress in the past two decades.
The traditional problem addressed by surface reconstruction is to recover the digital …

In this paper, we propose PCPNet, a deep‐learning based approach for estimating local 3D
shape properties in point clouds. In contrast to the majority of prior techniques that …

Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required
to be consolidated. In this paper, we present the first deep learning based {em edge-aware} …