An Immersive Labeling Method for Large Point Clouds

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


3D point clouds often require accurate labeling and semantic information. However, in the absence of fully automated methods, such labeling must be performed manually, which can prove extremely time and labour intensive. To address this, we propose a novel hybrid CPU/GPU-based algorithm allowing instantaneous selection and modification of points supporting very large point clouds. Our tool provides a palette of 3D interactions for efficient viewing, selection and labeling of points using head-mounted VR and controllers. We evaluate our method with 25 users on tasks involving large point clouds and find convincing results that support the use case of VR-based point cloud labeling.


Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023
Number of pages2
ISBN (electronic)979-8-3503-4839-2
ISBN (print)979-8-3503-4840-8
Publication statusPublished - Mar 2023

External IDs

Scopus 85159719587
Mendeley 0c5a3213-e3f5-31b9-9a2b-21b883e5089c
ORCID /0000-0002-3671-1619/work/142248366


Research priority areas of TU Dresden

Subject groups, research areas, subject areas according to Destatis


  • Human-centered computing, Immersive interaction, Immersive labeling, Virtual Reality