Learning based 2D Irregular Shape Packing

TL;DR

This paper introduces a learning-assisted method for 2D irregular shape packing in UV mapping, achieving higher packing ratios efficiently by combining neural policies with joint optimization.

Contribution

It proposes a novel learning-based approach that reduces the complex packing problem to bin-packing, enabling efficient and high-quality UV packing for 3D models.

Findings

Higher packing ratios than baseline methods

Linear time scaling with number of patches

Effective on multiple datasets

Abstract

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.