Work

Evaluating automatic label noise detection in 3D segmentation with realistic label noise

Year: 2026

Type: article

Abstract: Automatic label correction methods have been developed to address issues that come with a growing volume of annotated data in Machine Learning pipelines. The majority of these methods have been primarily evaluated on artificial noise in Euclidean data. Yet, particularly the subject of Geometric Deep Learning could benefit from well-performing label correction algorithms since non-Euclidean data, such as curved surfaces in 3D, is especially cumbersome and error prone to annotate. To assess the current applicability of existing label correction methods to 3D data, we evaluate automatic noise detection methods using realistic and artificial label noise in the 3D domain. We not only evaluate their effectiveness in 3D, but also examine general correction behavior regarding properties like carefulness and aggressiveness and compare those between both noise types. Our investigation shows that finding realistic noise in a 3D segmentation problem is of different nature compared to finding artificial noise.

Source: Neurocomputing

DOI: 10.1016/j.neucom.2026.133587

Authors: Andreas Mazur, Isaac Roberts, David P. Leins, Alexander Schulz, Barbara Hammer

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funders: Deutsche Forschungsgemeinschaft, Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen, HORIZON EUROPE Framework Programme, H2020 Marie Skłodowska-Curie Actions

Awards: G4235319083, G761758636, Datengetriebene Modellierung von mehrstufigen Stanzbiegeprozessen