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.
The aim of this paper is to provide a framework for the improvement of the processes involving risks evaluation and management evaluation in Central East Moldavian Region, considering the occurrence of natural disasters, such as floods, earthquakes, forest fires and landslides. Consequently, a GIS application has been developed in order to graphically reflect and analyze these risks. The studies on the targeted region would be a starting point to further extensions to other Romanian regions. The application will be publicly available in order to provide an early warning system to people, since no such services exist in Romania at individual level. Important aspects concerning risk management status and policies in Romania are also discussed.
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