Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning

by   Christina Petschnigg, et al.

The digital factory provides undoubtedly a great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of 3D data. In order to generate an accurate factory model including the major components, i.e. building parts, product assets and process details, the 3D data collected during digitalization can be processed with advanced methods of deep learning. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. This allows us to analyze how different ways of estimating uncertainty in these networks improve segmentation results on raw 3D point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks' uncertainty in their predictions. For evaluation we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information makes this approach especially applicable to safety critical applications.



There are no comments yet.


page 14


From a Point Cloud to a Simulation Model: Bayesian Segmentation and Entropy based Uncertainty Estimation for 3D Modelling

The 3D modelling of indoor environments and the generation of process si...

Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods

Segmentation of structural parts of 3D models of plants is an important ...

MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences

Understanding dynamic 3D environment is crucial for robotic agents and m...

Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds

Technology to recognize the type of component represented by a point clo...

Stein ICP for Uncertainty Estimation in Point Cloud Matching

Quantification of uncertainty in point cloud matching is critical in man...

Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark

This paper presents a new 3D point cloud classification benchmark data s...

A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty

Accurate rotation estimation is at the heart of robot perception tasks s...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.