  2D irregular shape packing is a necessary step to arrange UV patches of a 3D
model within a texture atlas for memory-efficient appearance rendering in
computer graphics. Being a joint, combinatorial decision-making problem
involving all patch positions and orientations, this problem has well-known
NP-hard complexity. Prior solutions either assume a heuristic packing order or
modify the upstream mesh cut and UV mapping to simplify the problem, which
either limits the packing ratio or incurs robustness or generality issues.
Instead, we introduce a learning-assisted 2D irregular shape packing method
that achieves a high packing quality with minimal requirements from the input.
Our method iteratively selects and groups subsets of UV patches into
near-rectangular super patches, essentially reducing the problem to
bin-packing, based on which a joint optimization is employed to further improve
the packing ratio. In order to efficiently deal with large problem instances
with hundreds of patches, we train deep neural policies to predict nearly
rectangular patch subsets and determine their relative poses, leading to linear
time scaling with the number of patches. We demonstrate the effectiveness of
our method on three datasets for UV packing, where our method achieves a higher
packing ratio over several widely used baselines with competitive computational
speed.
