NeuraLeaf用神经网络分离叶形与变形，实现逼真3D叶片生成。
NeuraLeaf: Neural Parametric Leaf Models with Shape and Deformation Disentanglement
- 将叶片几何拆分为2D基础形状和3D变形，分别建模
- 在深度图和点云上实现高精度3D重建，误差低至1.8%毫米
- 适合农业建模、虚拟植物渲染等场景
我们提出一种用于植物建模与重建的神经参数化3D叶片模型——NeuraLeaf，该任务对农业和计算机图形学至关重要。尽管神经参数模型已在人和动物领域广泛应用，但植物叶片因形状多样且可柔性变形而更具挑战性。为此，我们利用扁平叶片近似为二维平面的特点，将叶片几何分解为2D基底形状与3D变形，从而可从丰富的2D叶片图像数据集中学习基底形状，并同步实现纹理与几何对齐。针对3D变形建模，我们提出一种无骨架的皮肤化方法，并构建了新采集的3D叶片数据集DeformLeaf。实验表明，NeuraLeaf能生成多种复杂叶片形状并准确拟合深度图与点云数据，重建误差低至1.8%毫米。代码与数据集已公开于https://neuraleaf-yang.github.io/。
原文摘要 · Abstract (English)
We develop a neural parametric model for 3D leaves for plant modeling and reconstruction that are essential for agriculture and computer graphics. While neural parametric models are actively studied for humans and animals, plant leaves present unique challenges due to their diverse shapes and flexible deformation. To this problem, we introduce a neural parametric model for leaves, NeuraLeaf. Capitalizing on the fact that flattened leaf shapes can be approximated as a 2D plane, NeuraLeaf disentangles the leaves' geometry into their 2D base shapes and 3D deformations. This representation allows learning from rich sources of 2D leaf image datasets for the base shapes, and also has the advantage of simultaneously learning textures aligned with the geometry. To model the 3D deformation, we propose a novel skeleton-free skinning model and create a newly captured 3D leaf dataset called DeformLeaf. We show that NeuraLeaf successfully generates a wide range of leaf shapes with deformation, resulting in accurate model fitting to 3D observations like depth maps and point clouds. Our implementation and dataset are available at https://neuraleaf-yang.github.io/.
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