Getting Topology and Point Cloud Generation to Mesh
Carnegie Mellon University（卡内基梅隆大学）
英文摘要：
In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface. This view motivates a generative model that works by progressively deforming a uniform sphere until it approximates the goal point cloud. We review the underlying concepts leading to this conclusion from computer graphics and topology in differential geometry, and model the generation process as deformation via deep neural network parameterization. Finally, we show that this view of the problem produces a model that can generate quality meshes efficiently.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
