PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks
R. Roveri, A. C. Öztireli, I. Pandele, M. Gross
Proceedings of Eurographics (Delft, The Netherlands, April 16-20, 2018), Computer Graphics Forum, vol. 37, no. 2, pp. 87-99
Abstract
With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point
cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of
assumptions such as local smoothness to tackle this ill-posed problem. However, such priors lead to loss of important features
and geometric detail. Instead, we propose a novel data-driven approach for point cloud consolidation via a convolutional
neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud
accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to
reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network
architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use
this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud
consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons
to the state-of-the-art.
