3DV and Geometry
Geometry
Shape Deformation and Editing
1. DeformSyncNet: Deformation Transfer via Synchronized Shape Deformation Spaces
Shape Generation
1. Learning Representations and Generative Models for 3D Point Clouds
2. PolyGen: An Autoregressive Generative Model of 3D Meshes
3. ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
Single View Reconstruction
1. DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
2. Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
Geometry Feature Learning
1. PointCNN
2. PointNet
3. MeshCNN: A Network with an Edge
4. UV-Net: Learning from Curve-Networks and Solids
Differentiable Renderer
1. Neural Rerendering in the Wild
2. DFR: Differentiable Function Rendering for Learning 3D Generation from Images
Shape Analysis
1. SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
2. Symmetry in 3D Geometry: Extraction and Applications
3. Discovering Structural Regularity in 3D Geometry
4. PIE-NET: Parametric Inference of Point Cloud Edges
3D Vision
Localization
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Mapping
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3D Programming
Preliminary
1. Generate a Shape from X
2. Shape Editing
3. Shape Analysis
4. Special Topics on Face, Body and Facades
Advance
3DV and Geometry
Geometry
Shape-Generation
rawGAN.md
Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas Guibas
ICML 2018.
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2020, 3dv-geometry