gaussian-splatting-papers-guide

npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill gaussian-splatting-papers-guide --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 3D Gaussian Splatting Papers Guide ## Overview 3D Gaussian Splatting (3DGS) is a breakthrough technique for real-time radiance field rendering that represents scenes as collections of 3D Gaussians. This curated collection tracks the rapidly evolving 3DGS literature — from the original paper through extensions for dynamic scenes, generation, compression, SLAM, avatars, and more. Essential for researchers in computer vision, graphics, and neural rendering. ## Core Paper ```bibtex @inproceedings{kerbl3Dgaussians, title={3D Gaussian Splatting for Real-Time Radiance Field Rendering}, author={Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George}, booktitle={ACM SIGGRAPH 2023}, year={2023} } ``` ### Key Idea ``` Input: Multi-view images + SfM point cloud ↓ Initialize 3D Gaussians (position, covariance, color, opacity) ↓ Differentiable splatting (project Gaussians → image plane) ↓ Optimize via photometric loss ↓ Adaptive density control (clone, split, prune) ↓ Output: Real-time renderable 3D scene (100+ FPS) ``` ## Research Landscape ### Category Map | Category | Focus | Key Papers | |----------|-------|------------| | **Static Scenes** | Quality, compre

Original implementation
git clone https://github.com/graphdeco-inria/gaussian-splatting
cd gaussian-splatting
pip install -r requirements.txt
Train on custom scene
python train.py -s path/to/colmap/data
Real-time viewer