  We study the problem of symmetry detection of 3D shapes from single-view
RGB-D images, where severely missing data renders geometric detection approach
infeasible. We propose an end-to-end deep neural network which is able to
predict both reflectional and rotational symmetries of 3D objects present in
the input RGB-D image. Directly training a deep model for symmetry prediction,
however, can quickly run into the issue of overfitting. We adopt a multi-task
learning approach. Aside from symmetry axis prediction, our network is also
trained to predict symmetry correspondences. In particular, given the 3D points
present in the RGB-D image, our network outputs for each 3D point its symmetric
counterpart corresponding to a specific predicted symmetry. In addition, our
network is able to detect for a given shape multiple symmetries of different
types. We also contribute a benchmark of 3D symmetry detection based on
single-view RGB-D images. Extensive evaluation on the benchmark demonstrates
the strong generalization ability of our method, in terms of high accuracy of
both symmetry axis prediction and counterpart estimation. In particular, our
method is robust in handling unseen object instances with large variation in
shape, multi-symmetry composition, as well as novel object categories.
