Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks

He Wang, Zetian Jiang, Li Yi, Kaichun Mo, Hao Su, Leonidas Guibas

Keywords: 3D point cloud, GAN, sampling pattern, evaluation metrics, discriminator

Abstract: In this paper, we examine the long-neglected yet important effects of point sam- pling 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, PointConv, KPConv) 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, given that recent research has been focused on the generator design, the discriminator design needs more attention. Our work provides both suggestions and tools for building future discriminators. We will release the code to facilitate future research.

One-sentence Summary: we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs and found a functional 3D point cloud discriminator shouldn't be oversensitive to point sampling pattern.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Reviewed Version (pdf): https://openreview.net/references/pdf?id=uaq-Cn1dZ1