There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three
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There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three-dimensional data of our physical surroundings. Most of these applications require more than the shape of their surroundings. For example, a self-driving car needs to identify pedestrians, road signs and traffic lights in order to navigate safely. Therefore the point cloud acquired by laser scanning needs to be enriched with additional information. Automatic assignment of the object type a point belongs to is called classification.
This research focuses on deep learning for point cloud classification, because it revolutionized classification of imagery. PointNet is used to enable deep learning directly on point cloud data sets. To date PointNet is proven for indoor point clouds, this research explores the application of PointNet on an outdoor highway scene. The methodology creates point cloud training data efficiently by reusing known 2D object locations. Different spatial representations and sampling methods for the points are tested.
On average the method classifies 50% of the points correctly in four object classes. In combination with clustering of the point-wise predictions, the method predicts 60% of the 2D object locations successfully. The performance is comparable to the 47% average class accuracy PointNet achieves for 13 classes on the indoor data set.
