Uncertainty-aware point cloud segmentation for infrastructure projects using Bayesian deep learning

Automation in Construction, vol. 164, pp. 105419

Abstract

Reliable traffic infrastructure is a key factor for any country's economy. However, aging bridges often require renovation or reconstruction. Promising approaches for enhancing asset management and cost reduction are digital twins and predictive maintenance strategies. However, the creation of geometric-semantic as-is models as a basis for digital twins currently involves labor-intensive manual data capture and modeling. Modern deep learning models, such as Kernel Point Convolution (KPConv) show promising results in reducing the time needed to create digital twins by semantically segmenting point cloud data but have so far been hindered by the lack of a reliable quality measure, which can predict when the model's prediction can be trusted. This is especially viable in the construction industry, where objects and sensors may vary widely between projects and contractors. In this work, we present Bayesian neural networks, implemented through Variational Inference and Monte-Carlo dropout as approaches to conducting inference with KPConv, which show improvements in the confidence estimation and out-of-domain (OOD) detection in the scans of typical infrastructure point clouds. When confronted with different domain shifts to the test data, such as a change of scanning device and introduction of unseen classes the proposed model showed a 25.3% decrease in expected calibration error (ECE) and a 4.82 increase in OOD detection in terms of outlier-intersection-over-union (O-IoU) on average with respect to a deterministic baseline.

Authors 3

1. Hristo Vassilev Hristo Vassilev corresponding Aachen Geodetic Institute Chair for Computing in Civil Engineering & Geo Information Systems

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, Aachen 52074, North Rhine-Westphalia, Germany

2. Marius Laska Aachen Geodetic Institute Chair for Computing in Civil Engineering & Geo Information Systems

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, Aachen 52074, North Rhine-Westphalia, Germany

3. Jörg Blankenbach Aachen Geodetic Institute Chair for Computing in Civil Engineering & Geo Information Systems

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, Aachen 52074, North Rhine-Westphalia, Germany