论文标题
重新思考3D点云生成对抗网络中的采样
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
论文作者
论文摘要
在本文中，我们研究了点云中的点采样模式的长期结合但重要的效果。通过广泛的实验，我们表明对抽样不敏感的歧视因子（例如PointNet-Max）产生了具有点簇伪影的形状点云，而采样 - 跨敏感歧视器（例如PointNet ++，DGCNN，dgcnn）无法指导有效的形状产生。我们提出采样谱的概念来描述歧视因子的不同采样敏感性。我们进一步研究了不同的评估指标如何权衡抽样模式与几何形状，并提出了几种构成指标采样频谱的感知指标。在提出的采样频谱的指导下，我们发现了一个中间点采样感知的基线歧视器PointNet-Mix，该歧视器PointNet-Mix通过在与采样相关的度量方面的较大边距改善了所有现有的点云发生器。我们指出，尽管最近的研究集中在发电机设计上，但点云的主要瓶颈实际上在于歧视器设计。我们的工作提供了建立未来歧视者的建议和工具。我们将发布代码以促进未来的研究。
In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive discriminators (e.g.PointNet-Max) produce shape point clouds with point clustering artifacts while sampling-oversensitive discriminators (e.g.PointNet++, DGCNN) fail to guide valid shape generation. We propose the concept of sampling spectrum to depict the different sampling sensitivities of discriminators. We further study how different evaluation metrics weigh the sampling pattern against the geometry and propose several perceptual metrics forming a sampling spectrum of metrics. Guided by the proposed sampling spectrum, we discover a middle-point sampling-aware baseline discriminator, PointNet-Mix, which improves all existing point cloud generators by a large margin on sampling-related metrics. We point out that, though recent research has been focused on the generator design, the main bottleneck of point cloud GAN actually lies in the discriminator design. Our work provides both suggestions and tools for building future discriminators. We will release the code to facilitate future research.
