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 …

We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in
graph neural networks (GNNs) and their evolution with explainable artificial intelligence …

Neural implicit representation is a promising approach for reconstructing surfaces from point
clouds. Existing methods combine various regularization terms, such as the Eikonal and …

Automatic methods for reconstructing buildings from airborne Lidar point clouds focus on
producing accurate 3D models in a fast and scalable manner but they overlook the problem …

Plane arrangements are a useful tool for surface and volume modelling. However, their main
drawback is poor scalability. We introduce two key novelties that enable the construction of …

Surface reconstruction is a challenging task when input point clouds, especially real scans,
are noisy and lack normals. Observing that the Multilayer Perceptron (MLP) and the implicit …

Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point
clouds. In this work, we show that state-of-the-art techniques work by minimizing an …

To address the inaccurate classification of coarse aggregate particle size due to insufficient
height information in single‐view, a multi‐view and graph convolutional network (GCN) …

Neural implicit representation, the parameterization of distance function as a coordinate
neural field, has emerged as a promising lead in tackling surface reconstruction from …

Existing methods for parametric surface reconstruction from 3D point data typically segment
the points into multiple subsets, each fitted with a parametric surface patch. These methods …