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Continuous Geodesic Convolutions for Learning on 3D Shapes
In this work, we wish to challenge this practice and use a neural network to learn descriptors directly from the raw mesh.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.
3D Conceptual Design Using Deep Learning
Through this project, we expect the output can show a clear and smooth interpretation of model from different categories to develop a fast design support to generate novel shapes.
Data-driven Upsampling of Point Clouds
High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.
