Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
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发表时间：
2020-06
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通讯作者：
He Wang;Zetian Jiang;Li Yi;Kaichun Mo;Hao Su;L. Guibas
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作者：
He Wang;Zetian Jiang;Li Yi;Kaichun Mo;Hao Su;L. Guibas
在本文中，我们研究了点云GAN中点采样模式长期被忽视但重要的影响。通过大量的实验，我们表明，采样不敏感的鉴别器（例如PointNet-Max）产生的形状点云与点聚类工件，而采样过度敏感的鉴别器（例如PointNet++，DGCNN）未能指导有效的形状生成。我们提出了采样谱的概念来描述鉴别器的不同采样灵敏度。我们进一步研究了不同的评价指标如何衡量采样模式对几何形状，并提出了几个感知指标形成一个采样谱的度量。在所提出的采样谱的指导下，我们发现了一个中间点采样感知的基线模型PointNet-Mix，它在采样相关指标上大幅提高了所有现有的点云生成器。我们指出，虽然最近的研究一直集中在生成器的设计，点云GAN的主要瓶颈实际上在于可重构设计。我们的工作为构建未来的判别器提供了建议和工具。我们将发布代码以方便未来的研究。
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.