Adaptive feature-conserving compression for large scale point clouds

Felix Eickeler, André Borrmann

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this work we introduce a practical method for reducing big point clouds of buildings and infrastructure. The proposed method introduces bilateral filtering with a tailored set of evaluation functions, that will conserve as much information as possible. To determin the actual statistical parameters to perform this filtering and reason about our development, we investigate different point properties on a comprehensive dataset. The dataset contains artificial, photogrammetric and laser scanned point clouds and was made publicly available. We showcase our filtering method by preserving more information than voxel grid or density filters challenging even sparser photogrammetric datasets. Finally, we discuss some encoding strategies as well as the sweet spot between size and resolution.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2394
StatePublished - 2019
Event26th International Workshop on Intelligent Computing in Engineering, EG-ICE 2019 - Leuven, Belgium
Duration: 30 Jun 20193 Jul 2019

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