1 research outputs found
An octree cells occupancy geometric dimensionality descriptor for massive on-server point cloud visualisation and classification
Lidar datasets are becoming more and more common. They are appreciated for
their precise 3D nature, and have a wide range of applications, such as surface
reconstruction, object detection, visualisation, etc. For all this
applications, having additional semantic information per point has potential of
increasing the quality and the efficiency of the application. In the last
decade the use of Machine Learning and more specifically classification methods
have proved to be successful to create this semantic information. In this
paradigm, the goal is to classify points into a set of given classes (for
instance tree, building, ground, other). Some of these methods use descriptors
(also called feature) of a point to learn and predict its class. Designing the
descriptors is then the heart of these methods. Descriptors can be based on
points geometry and attributes, use contextual information, etc. Furthermore,
descriptors can be used by humans for easier visual understanding and sometimes
filtering. In this work we propose a new simple geometric descriptor that gives
information about the implicit local dimensionality of the point cloud at
various scale. For instance a tree seen from afar is more volumetric in nature
(3D), yet locally each leaves is rather planar (2D). To do so we build an
octree centred on the point to consider, and compare the variation of the
occupancy of the cells across the levels of the octree. We compare this
descriptor with the state of the art dimensionality descriptor and show its
interest. We further test the descriptor for classification within the Point
Cloud Server, and demonstrate efficiency and correctness results.Comment: extracted from article arXiv:1602.06920 ( arXiv:1602.06920 was split
into 2 articles because it was to long and to hard to read