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Improving Object Detection and Classification from 3D LiDAR Scans

Student: Aleksandr Borzunov

Supervisor: Artem Babenko

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2020

In this paper, we study the possibility of adapting a neural network architecture for anchor-free object detection FoveaBox for the case of processing 3D point clouds. We consider the problem of 3D object detection for predefined classes, as well as searching for arbitrary moving objects on the scene. We demonstrate that the resulting anchor-free architecture does not lose much quality and allows to create smaller, faster and simpler models that are more robust in predicting the size of arbitrary dynamic objects.

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