Abstract
This paper presents a descriptor-based method for labeling point clouds using a two-stage transformer architecture. The first stage consists of an encoder that extracts descriptors from point cloud fragments. The second stage, a decoder, assigns labels to these fragments based on both the descriptor of the current fragment and an earlier predefined pattern descriptor. This approach functions as an interactive labeling tool similar to a brush, with the ability to reinforce or weaken the pattern through direct manipulation of its descriptor.
Paper Type
Poster
DOI
10.62036/ISD.2025.65
Interactive Semi-Automatic Labeling of Point Clouds Using Transformer-Based Descriptors
This paper presents a descriptor-based method for labeling point clouds using a two-stage transformer architecture. The first stage consists of an encoder that extracts descriptors from point cloud fragments. The second stage, a decoder, assigns labels to these fragments based on both the descriptor of the current fragment and an earlier predefined pattern descriptor. This approach functions as an interactive labeling tool similar to a brush, with the ability to reinforce or weaken the pattern through direct manipulation of its descriptor.
Recommended Citation
Najgebauer, P., Scherer, R., Walczak, J. & Wojciechowski, A. (2025). Interactive Semi-Automatic Labeling of Point Clouds Using Transformer-Based DescriptorsIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.65