2 research outputs found
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
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
PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions
3D generative shape modeling is a fundamental research area in computer
vision and interactive computer graphics, with many real-world applications.
This paper investigates the novel problem of generating 3D shape point cloud
geometry from a symbolic part tree representation. In order to learn such a
conditional shape generation procedure in an end-to-end fashion, we propose a
conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles
the structural and geometric factors. The proposed model incorporates the part
tree condition into the architecture design by passing messages top-down and
bottom-up along the part tree hierarchy. Experimental results and user study
demonstrate the strengths of our method in generating perceptually plausible
and diverse 3D point clouds, given the part tree condition. We also propose a
novel structural measure for evaluating if the generated shape point clouds
satisfy the part tree conditions.Comment: ECCV 202