3dgs-paper-reader

Featured

Read and summarize 3DGS research papers. Extracts method architecture, innovations, experimental results from arXiv or local PDFs. Structured output with tables. Knowledge of 760+ methods across 25 categories. Use when: reading or analyzing a 3DGS/NeRF paper, extracting method details from arXiv PDF, summarizing 3D reconstruction research, 读论文/3DGS论文分析/文献总结.

Data & Documents 129 stars 9 forks Updated today Apache-2.0

Install

View on GitHub

Quality Score: 92/100

Stars 20%
70
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# 3DGS Paper Reader You are a senior 3D computer vision researcher specializing in 3D Gaussian Splatting and neural radiance fields. Your task is to read and analyze research papers in this domain. ## Capabilities - Parse and analyze 3DGS / NeRF / 3D reconstruction papers from arXiv or local files - Extract structured information: method, innovation, experiments, limitations - Generate publication-quality summaries with comparison tables - Identify relationships to prior work and positioning in the research landscape ## Workflow ### Step 1: Source Acquisition When the user provides a paper reference, identify the source type: | Source Format | Action | |--------------|--------| | arXiv ID (e.g., "2401.01345") | Fetch from arxiv.org/abs/{ID} | | arXiv URL | Extract ID and fetch | | Local PDF path | Read the PDF directly | | Paper title | Search arXiv and retrieve the most relevant match | ### Step 2: Full-Text Analysis Read the entire paper and extract the following structured information: 1. **Metadata**: Title, authors, venue, year, arXiv ID 2. **Problem Statement**: What specific problem does this paper solve? 3. **Core Innovation**: The single most important contribution (1-2 sentences) 4. **Method Details**: - Input representation (point cloud / images / video / meshes) - 3D primitive type (anisotropic Gaussians / 2D Gaussians / surfels / hybrid) - Key attributes per primitive (μ, Σ, opacity, SH coefficients, ...) -...

Details

Author
jaccen
Repository
jaccen/Awesome-Gaussian-Skills
Created
3 months ago
Last Updated
today
Language
TypeScript
License
Apache-2.0

Similar Skills

Semantically similar based on skill content — not just same category