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Found 9 papers, 1 papers with code
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes.
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.
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.
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.
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.
Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence
This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics.
TetCNN: Convolutional Neural Networks on Tetrahedral Meshes
Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.
Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering
Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings.
OmniMotionGPT: Animal Motion Generation with Limited Data
Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset.
