  Assembling parts into an object is a combinatorial problem that arises in a
variety of contexts in the real world and involves numerous applications in
science and engineering. Previous related work tackles limited cases with
identical unit parts or jigsaw-style parts of textured shapes, which greatly
mitigate combinatorial challenges of the problem. In this work, we introduce
the more challenging problem of shape assembly, which involves textureless
fragments of arbitrary shapes with indistinctive junctions, and then propose a
learning-based approach to solving it. We demonstrate the effectiveness on
shape assembly tasks with various scenarios, including the ones with abnormal
fragments (e.g., missing and distorted), the different number of fragments, and
different rotation discretization.
