Skip to main content
Top

Hint

Swipe to navigate through the chapters of this book

2021 | OriginalPaper | Chapter

Uncertainty aware deep point based neural network for 3D object classification

Authors : Christina Petschnigg, Jürgen Pilz

Published in: Data Science – Analytics and Applications

Publisher: Springer Fachmedien Wiesbaden

share
SHARE

Efforts in various planning scenarios like factory planning, motion and trajectory planning, product design, etc. tend towards full realization in 3D. This makes point clouds an important 3D data type for capturing and assessing different situations. In this paper, we design a Bayesian extension to the frequentist PointNet classification network [1] by applying Bayesian convolutions and linear layers with variational inference. This approach allows the estimation of the model’s uncertainty in its predictions. Further, we are able to describe how each point in the input point cloud contributes to the prediction level uncertainty. Additionally, our network is compared against the state-of-the-art and shows strong performance. We prove the feasibility of our approach using a ModelNet 3D data set. Further, we generate an industrial 3D point data set at a German automotive assembly plant and apply our network. The results show that we can improve the frequentist baseline on ModelNet by about 6.46 %.

Metadata
Title
Uncertainty aware deep point based neural network for 3D object classification
Authors
Christina Petschnigg
Jürgen Pilz
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-658-32182-6_11

Premium Partner