Abstract
Matching local geometric features on real-world depth
images is a challenging task due to the noisy, lowresolution, 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.