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3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) oral presentation, July 2017

Andy Zeng, Shuran Song, Matthias Nießner,
Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
Abstract

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu
Citation

Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser.
"3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions."
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) oral presentation, July 2017.

BibTeX

@inproceedings{Zeng:2017:3LL,
   author = "Andy Zeng and Shuran Song and Matthias Nie{\ss}ner and Matthew Fisher
      and Jianxiong Xiao and Thomas Funkhouser",
   title = "{3DMatch}: Learning Local Geometric Descriptors from {RGB-D}
      Reconstructions",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR) oral
      presentation",
   year = "2017",
   month = jul
}