Research map / 3D vision / Face and shape
From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
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.
Closest papers by meaning
- NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries (NeurIPS 2023 · similarity 0.88)
- GeoNet: Deep Geodesic Networks for Point Cloud Analysis (CVPR 2019 · similarity 0.88)
- Wormhole Loss for Partial Shape Matching (NeurIPS 2024 · similarity 0.86)
- Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models (NeurIPS 2025 · similarity 0.85)
- Grounding Continuous Representations in Geometry: Equivariant Neural Fields (ICLR 2025 · similarity 0.85)
- Connecting Neural Models Latent Geometries with Relative Geodesic Representations (NeurIPS 2025 · similarity 0.85)
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.