Publication: Object detection and classification from large-scale cluttered indoor scans
Object detection and classification from large-scale cluttered indoor scans
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Mattausch, O., Panozzo, D., Mura, C., Sorkine-Hornung, O., & Pajarola, R. (2014). Object detection and classification from large-scale cluttered indoor scans. Computer Graphics Forum, 33, 11–21. https://doi.org/10.1111/cgf.12286
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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, enabling execution at interactive rates.
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- Mattausch, Oliver
- Panozzo, Daniele
- Mura, Claudio
- Sorkine-Hornung, Olga
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Mattausch, O., Panozzo, D., Mura, C., Sorkine-Hornung, O., & Pajarola, R. (2014). Object detection and classification from large-scale cluttered indoor scans. Computer Graphics Forum, 33, 11–21. https://doi.org/10.1111/cgf.12286