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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.
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.
Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms.