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.

翻译：二维不规则形状排列是计算机图形学中用于将三维模型的 UV 贴片排列到纹理图集中以实现内存高效外观渲染的必要步骤。这是一个涉及所有贴片位置和方向的联合组合决策问题，具有众所周知的 NP 难度复杂性。先前的解决方案要么假设启发式排列顺序，要么修改上游网格切割和 UV 映射以简化问题，这要么限制了排列比率，要么带来了鲁棒性或通用性问题。相反，我们引入了一种学习辅助的二维不规则形状排列方法，该方法在输入要求极低的情况下实现了高排列质量。我们的方法迭代地选择 UV 贴片子集并将其分组为近矩形的超级贴片，从而将问题简化为装箱问题，并在此基础上采用联合优化进一步提高排列比率。为了高效处理具有数百个贴片的大规模问题实例，我们训练深度神经策略来预测近乎矩形的贴片子集并确定其相对姿态，从而实现了与贴片数量线性相关的计算时间尺度。我们在三个用于 UV 排列的数据集上展示了我们方法的有效性，与几种广泛使用的基线方法相比，我们的方法以竞争力的计算速度实现了更高的排列比率。