Three-dimensional (3-D) urban models are an integral part of numerous applications, such
as urban planning and performance simulation, mapping and visualization, emergency …

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete
point cloud observation is a long-standing problem in computer vision and graphics …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

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 …

We present a benchmark for image-based 3D reconstruction. The benchmark sequences
were acquired outside the lab, in realistic conditions. Ground-truth data was captured using …

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD)
models for research of geometric deep learning methods and applications. Each model is a …

We propose a generative adversarial network for point cloud upsampling, which can not
only make the upsampled points evenly distributed on the underlying surface but also …

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 …