论文标题
持续隐式形状表示的对抗生成
Adversarial Generation of Continuous Implicit Shape Representations
论文作者
论文摘要
这项工作提出了一种生成的对抗架构，用于基于签名距离表示生成三维形状。虽然深层形状主要是通过体素和表面云的方法来解决的，但我们的发电机学会了在给定的潜在信息的空间中近似签名的距离。尽管结构上与生成点云方法相似，但可以在推理过程中以任意点密度评估该公式，从而在生成的输出中获得细粒度的细节。此外，我们研究了使用逐步发展体素或点处理网络作为歧视者的效果，并提出了一种改进方案，以增强发电机在形状的零ISO地表决策边界上建模零的能力。我们在Shapenet基准数据集上训练方法，并在定量和定性上验证其在生成逼真的3D形状方面的性能。
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has been mostly tackled by voxel and surface point cloud approaches, our generator learns to approximate the signed distance for any point in space given prior latent information. Although structurally similar to generative point cloud approaches, this formulation can be evaluated with arbitrary point density during inference, leading to fine-grained details in generated outputs. Furthermore, we study the effects of using either progressively growing voxel- or point-processing networks as discriminators, and propose a refinement scheme to strengthen the generator's capabilities in modeling the zero iso-surface decision boundary of shapes. We train our approach on the ShapeNet benchmark dataset and validate, both quantitatively and qualitatively, its performance in generating realistic 3D shapes.
