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 …

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 …

Point clouds acquired from scanning devices are often perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In
this paper, we exploit range images generated from 3D LiDAR scans to address the problem …

In this work we target a learnable output representation that allows continuous, high
resolution outputs of arbitrary shape. Recent works represent 3D surfaces implicitly with a …

We present the first end-to-end solution to create high-quality free-viewpoint video encoded
as a compact data stream. Our system records performances using a dense set of RGB and …

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 …

The reconstruction of a discrete surface from a point cloud is a fundamental geometry
processing problem that has been studied for decades, with many methods developed. We …

We show that surface reconstruction from oriented points can be cast as a spatial Poisson
problem. This Poisson formulation considers all the points at once, without resorting to …

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 …