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Point-clouds are a popular choice for vision and graphics tasks due to theiraccurate shape description and direct acquisition from range-scanners. Thisdemands the ability to synthesize and reconstruct high-quality point-clouds.Current deep generative models for 3D data generally work on simplifiedrepresentations (e.g., voxelized objects) and cannot deal with the inherentredundancy and irregularity in point-clouds. A few recent efforts on 3Dpoint-cloud generation offer limited resolution and their complexity grows withthe increase in output resolution. In this paper, we develop a principledapproach to synthesize 3D point-clouds using a spectral-domain GenerativeAdversarial Network (GAN). Our spectral representation is highly structured andallows us to disentangle various frequency bands such that the learning task issimplified for a GAN model. As compared to spatial-domain generativeapproaches, our formulation allows us to generate arbitrary number of pointshigh-resolution point-clouds with minimal computational overhead. Furthermore,we propose a fully differentiable block to transform from {the} spectral to thespatial domain and back, thereby allowing us to integrate knowledge fromwell-established spatial models. We demonstrate that Spectral-GAN performs wellfor point-cloud generation task. Additionally, it can learn {a} highlydiscriminative representation in an unsupervised fashion and can be used toaccurately reconstruct 3D objects.

Spectral-GANs for High-Resolution 3D Point-cloud Generation