Research map / Graph learning / GNN architectures
Surface Networks
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use mesh-based Laplacian propagation instead of point-cloud set aggregation | Surface Network · Temporal prediction test set | Smooth L1-loss: 23.64 (0.21) -> 17.34 (0.52) | Helped |
| Use mesh-based Dirac propagation instead of point-cloud set aggregation | Surface Network · Temporal prediction test set | Smooth L1-loss: 23.64 (0.21) -> 16.84 (0.16) | Helped |
| Replace Laplacian propagation with Dirac propagation (ablation) | Surface Network · Temporal prediction test set | Smooth L1-loss: 17.34 (0.52) -> 16.84 (0.16) | Helped |
| Use mesh-based Laplacian propagation instead of an MLP | Surface Network · Temporal prediction test set | Smooth L1-loss: 64.56 (0.62) -> 17.34 (0.52) | Helped |
| Use mesh-based Dirac propagation instead of an MLP | Surface Network · Temporal prediction test set | Smooth L1-loss: 64.56 (0.62) -> 16.84 (0.16) | Helped |
Closest papers by meaning
- Spectro-Riemannian Graph Neural Networks (ICLR 2025 · similarity 0.87)
- Improving Neural Network Surface Processing with Principal Curvatures (NeurIPS 2024 · similarity 0.87)
- Dynamic Neural Surfaces for Elastic 4D Shape Representation and Analysis (CVPR 2025 · similarity 0.87)
- Graph Geometry-Preserving Autoencoders (ICML 2024 · similarity 0.86)
- An Intrinsic Vector Heat Network (ICML 2024 · similarity 0.86)
- Geometry-Informed Neural Networks (ICML 2025 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.