Real-time compression of point cloud streams
By J. Kammerl, Nico Blodow, Radu Bogdan Rusu, Suat Gedikli, Michael Beetz, Eckehard G. Steinbach. IEEE International Conference on Robotics and Automation, 2012
353 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/4c57da9c0367eaa1df3f9fb27281e9307434ad21
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Graphics and Computer-Aided Design
- O. Nakagami
- Pablo César
- Cyrill Stachniss
- Kees Blom
- Ali J. Tabatabai
- Rufael Mekuria
- Wolfram Burgard
- Maren Bennewitz
In Computer Vision and Pattern Recognition
- Cyrill Stachniss
- Maren Bennewitz
- Armin Hornung
- Kai M. Wurm
- Wolfram Burgard
- O. Nakagami
- Pablo César
- P. Chou
Citations in the corpus
- OctoMap: an efficient probabilistic 3D mapping framework based on octrees (2013, background): “Another popular use case is the compression of static point clouds (Schnabel and Klein, 2006) or point cloud streams (Kammerl et al., 2012).”
- Design, Implementation, and Evaluation of a Point Cloud Codec for Tele-Immersive Video (2017, methodology): “we made comparisons with the available real-time point cloud codec in [3] with octree composition and DPCM coding.”
- Emerging MPEG Standards for Point Cloud Compression (2019, background): “Previous compression solutions for volumetric visual representations either focused on computer-generated content [1], [4] or suffered from low spatial and temporal compression performance [5], [6] when dealing with captured natural content.”