Reproduce “SGCR: Spherical Gaussians for Efficient 3D Curve Reconstruction” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xinran Yang, Donghao Ji, Yuanqi Li, Jie Guo, Yanwen Guo, Junyuan Xie
A method called SGCR enables efficient 3D curve reconstruction from multi-view images through a novel Spherical Gaussians representation and parametric curve extraction algorithm, achieving superior accuracy on benchmark datasets while reducing training time compared to existing approaches.
Key insights
- Fixed spherical covariance and grayscale values enable better edge representation than standard 3D Gaussians
- Two-phase training strategy with densification and pruning improves convergence
- Rational Bézier curve fitting with endpoint constraints produces connected curve networks
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.