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Abstract
Easily accessible depth sensors have enabled using point-cloud data to augment tabletop surfaces in everyday environments. However, point-cloud operations are computationally expensive and challenging to perform in real-time, particularly when targeting embedded systems without a dedicated GPU. In this paper, we propose mitigating the high computational costs by segmenting candidate interaction regions near real-time. We contribute an open-source solution for variable depth cameras using CPU-based architectures. For validation, we employ Microsoft’s Azure Kinect and report achieved performance. Our initial findings show that our approach takes under to segment candidate interaction regions on a tabletop surface and reduces the data volume by up to 70%. We conclude by contrasting the performance of our solution against a model-fitting approach implemented by the SurfaceStreams toolkit. Our approach outperforms the RANSAC-based strategy within the context of our test scenario, segmenting a tabletop’s interaction region up to 94% faster. Our results show promise for point-cloud-based approaches, even when targeting embedded solutions with limited resources.
Original language | English |
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Title of host publication | ISS 2021 - Companion Proceedings of the 2021 Conference on Interactive Surfaces and Spaces |
Number of pages | 5 |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery (ACM) |
Publication date | 14 Nov 2021 |
Pages | 33–37 |
ISBN (Electronic) | 978-1-4503-8340-0 |
DOIs | |
Publication status | Published - 14 Nov 2021 |
Event | ISS '21: Interactive Surfaces and Spaces - Lodz, Poland Duration: 14 Nov 2021 → 17 Nov 2021 |
Conference
Conference | ISS '21: Interactive Surfaces and Spaces |
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Country/Territory | Poland |
City | Lodz |
Period | 14/11/2021 → 17/11/2021 |
Series | ISS '21 |
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Keywords
- depth cameras
- fast 3D point-cloud segmentation
- interactive tabletop surfaces
Fingerprint
Dive into the research topics of 'Fast 3D Point-Cloud Segmentation for Interactive Surfaces'. Together they form a unique fingerprint.Projects
- 1 Finished
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VIGITIA: Vernetzte Intelligente Gegenstände durch, auf und um interaktive Tische im Alltag
Wimmer, R. (PI), Echtler, F. (CoI), Mthunzi, E. M. (Project Participant) & Maierhöfer, V. (Project Participant)
Bundesministerium für Bildung und Forschung (BMBF)
01/07/2019 → 30/06/2022
Project: Research