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

@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
}