Object detection and classification from large-scale cluttered indoor scans

@article{Mattausch2014ObjectDA,
  title={Object detection and classification from large-scale cluttered indoor scans},
  author={Oliver Mattausch and Daniele Panozzo and Claudio Mura and Olga Sorkine-Hornung and Renato Pajarola},
  journal={Comput. Graph. Forum},
  year={2014},
  volume={33},
  pages={11-21}
}
We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of our method… CONTINUE READING
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