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Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #1 on Point Cloud Generation on ShapeNet Car
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.
In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN.
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension.
Ranked #1 on Point Cloud Generation on ShapeNet Airplane
Generative models have proven effective at modeling 3D shapes and their statistical variations.