We propose a deep learning approach for finding dense correspondences between
3D scans of people. Our method requires only partial geometric information in
the form of two depth maps or partial reconstructed surfaces, works for humans
in arbitrary poses and wearing any clothing, does not require the two people to
be scanned from similar viewpoints, and runs in real time. We use a deep
convolutional neural network to train a feature descriptor on depth map pixels,
but crucially, rather than training the network to solve the shape
correspondence problem directly, we train it to solve a body region
classification problem, modified to increase the smoothness of the learned
descriptors near region boundaries. This approach ensures that nearby points on
the human body are nearby in feature space, and vice versa, rendering the
feature descriptor suitable for computing dense correspondences between the
scans. We validate our method on real and synthetic data for both clothed and
unclothed humans, and show that our correspondences are more robust than is
possible with state-of-the-art unsupervised methods, and more accurate than
those found using methods that require full watertight 3D geometry.

本研究提出了一种深度学习方法，用于在人体的 3D 扫描之间寻找密集的对应关系，深度卷积神经网络用于训练特征描述符，通过区域分类问题解决多种问题，并验证我们的方法在真实数据和合成数据上都表现出了优异的稳定性和准确性。