Neural Models for 3D Geometry

Course Description: Generation of high-quality 3D models and scenes by leveraging machine learning tools and approaches. Survey of geometry representations. Public 3D object and scene data sets. Neural architectures for geometry, including deep architectures for point clouds and meshes. Generative models for 3D: autoencoders, GANs, neural implicits, neural ODEs, autoregressive models. Conditional generation based on images or partial geometry. Variation generation. Evaluation metrics for content generation. Use of synthetic data in ML training pipelines. Prerequisites: CS148 and the rudiments of deep learning. Recommended: CS229.