Abstract
While the semantic segmentation of 2D images is
already a well-researched field, the assignment of
semantic labels to 3D data is lagging behind. This
is partly due to the fact that prelabeled training
data is only rarely available since not only the
training and application of classification methods
but also the manual labeling process are much more
time-consuming in 3D. This paper focuses on the
more classical approach of first calculating
features and subsequently applying a classification
algorithm. Existing handcrafted feature definitions
are enhanced by using multiple selected reductions
of the point cloud as approximations. This serves as
input to train a well-studied random forest
classifier. A comparison to a recently presented
deep learning approach, i.e., the Kernel Point
Convolution method, reveals that there are
well-justified applications for both modern and
classical machine learning methods. To enable the
smooth conversion of existing 3D scenes to
semantically labeled 3D point clouds the tool
Blender2Helios is presented. We show that
the therewith generated artificial data is a good
choice for training real-world classifiers.
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