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Using neural networks to detect objects in MLS point clouds based on local point neighborhoods

: Borgmann, Björn; Hebel, Marcus; Arens, Michael; Stilla, Uwe

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Stilla, U. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
PIA 19+MRSS 19, Photogrammetric Image Analysis & Munich Remote Sensing Symposium : Joint ISPRS conference, 18-20 September 2019, Munich, Germany
Istanbul: ISPRS, 2019 (ISPRS Annals IV-2/W7)
Workshop "Photogrammetric Image Analysis" (PIA) <2019, Munich>
Munich Remote Sensing Symposium (MRSS) <2019, Munich>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
mobile laser scanning; neural network; object detection; pedestrian

This paper presents an approach which uses a PointNet-like neural network to detect objects of certain types in MLS point clouds. In our case, it is used for the detection of pedestrians, but the approach can easily be adapted to other object classes. In the first step, we process local point neighborhoods with the neural network to determine a descriptive feature. This is then further processed to generate two outputs of the network. The first output classifies the neighborhood and determines if it is part of an object of interest. If this is the case, the second output determines where it is located in relation to the object center. This regression output allows us to use a voting process for the actual object detection. This processing step is inspired by approaches based on implicit shape models (ISM). It is able to deal with a certain amount of incorrectly classified neighborhoods, since it combines the results of multiple neighborhoods for the detection of an object. A benefit of our approach as compared to other machine learning methods is its low demand for training data. In our experiments, we achieved a promising detection performance even with less than 1000 training examples.