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

Andy Zeng, Shuran Song, Matthias Niessner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1802-1811

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

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[bibtex]
@InProceedings{Zeng_2017_CVPR,
author = {Zeng, Andy and Song, Shuran and Niessner, Matthias and Fisher, Matthew and Xiao, Jianxiong and Funkhouser, Thomas},
title = {3DMatch: Learning Local Geometric Descriptors From RGB-D Reconstructions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}