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awesome-gaussians

This repository tracks the latest advancements in 3D Gaussian Splatting from Arxiv, with daily automated updates. Stay up-to-date with cutting-edge research in this exciting field!

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longxiang-ai

Dernière activité 29 sept. 2026
longxiang-ai/awesome-gaussians

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2d-gaussian-splatting3d3d-gaussian-splatting3d-reconstruction4d-gaussian-splattingawesomegaussiangaussian-splattingsurface-reconstruction

Ce README est souvent en anglais.

Awesome Gaussian Splatting Awesome

A curated list of latest research papers, projects and resources related to Gaussian Splatting. Content is automatically updated daily.

🗺️ Explore the Paper Atlas: an interactive paper map, monthly trends, topic network and co-author network of every tracked paper.

Last Update: 2026-10-09 03:36:27

📰 Latest Updates

🗺️ [2026-09-30] Paper Atlas on GitHub Pages

  • New interactive site built from all daily snapshots, rebuilt after every update

🔧 [2026-08-08] Resilient arXiv Updates

  • Switched the crawler to the official HTTPS export API endpoint
  • Added bounded retries for rate limits, server errors, and network timeouts
  • Temporary arXiv outages now preserve existing data and finish scheduled runs with a warning
  • Added atomic, non-empty JSON writes and fallback to the latest valid data when generating README

📚 [2026-08-08] Community Paper Added

  • Added the HDR Gaussian Splatting paper suggested in Issue #3

🚀 [2026-02] Major Feature Update — v2.0

  • Unified CLI: Single entry point python main.py with subcommands: init, search, suggest, export-bib, readme
  • Interactive Configuration Wizard: Run python main.py init to set up keywords, domains, time range, and API keys step-by-step
  • Custom Time Range Filtering: Support relative periods (6m, 1y, 2y) and absolute date ranges (2024-01-01 to 2025-06-01)
  • Smart Link Extraction: Automatically extracts and classifies GitHub, project page, dataset, video, demo, and HuggingFace links from paper abstracts
  • BibTeX Export: Fetch BibTeX from arXiv and export to .bib files with category/date filters
  • LLM Keyword Suggestion: Paste a few paper titles or arXiv IDs, and an LLM automatically generates optimized search keywords
  • arXiv Domain Filtering: Restrict searches to specific arXiv categories (e.g., cs.CV, cs.GR)

🔧 [2025-06-26] Configurable Search Keywords Added

  • You can now customize search keywords by modifying data/search_config.json

  • View detailed updates: News.md 📋

Categories

  • 3DGS Surveys (5 papers) - Survey papers and benchmarks about 3D Gaussian Splatting
  • Acceleration (81 papers) - Papers about speeding up rendering or training
  • Applications (500 papers) - Papers about specific applications
  • Avatar Generation (170 papers) - Papers about human avatar generation
  • Dynamic Scene (192 papers) - Papers about dynamic scene reconstruction and rendering
  • Few-shot (39 papers) - Papers about few-shot or sparse view reconstruction
  • Geometry Reconstruction (210 papers) - Papers about 3D geometry reconstruction
  • Large Scene (25 papers) - Papers about large-scale scene reconstruction
  • Model Compression (205 papers) - Papers about model compression and optimization
  • Quality Enhancement (71 papers) - Papers focusing on improving rendering quality
  • Ray Tracing (4 papers) - Papers about ray tracing and ray casting in Gaussian Splatting
  • Relighting (40 papers) - Papers about relighting and illumination effects in Gaussian Splatting
  • SLAM (76 papers) - Papers about SLAM using Gaussian Splatting
  • Scene Understanding (98 papers) - Papers about scene understanding and semantic analysis

Table of Contents

Categorized Papers

3DGS Surveys

Acceleration

Showing the latest 50 out of 81 papers

Applications

Showing the latest 50 out of 500 papers

Avatar Generation

Showing the latest 50 out of 170 papers

Dynamic Scene

Showing the latest 50 out of 192 papers

Few-shot

Geometry Reconstruction

Showing the latest 50 out of 210 papers

Large Scene

Model Compression

Showing the latest 50 out of 205 papers

Quality Enhancement

Showing the latest 50 out of 71 papers

Ray Tracing

Relighting

SLAM

Showing the latest 50 out of 76 papers

Scene Understanding

Showing the latest 50 out of 98 papers

Classic Papers

Open Source Projects

Applications

Tutorials & Blogs

📋 Project Features

🛠️ Core Features

  • Unified CLI (main.py): Single entry point with init, search, suggest, export-bib, readme subcommands
  • Interactive Config Wizard: Guided setup for keywords, domains, time range, and API keys via python main.py init
  • Custom Search Keywords: Configure keywords for title, abstract, or both; with arXiv domain filtering (cs.CV, cs.GR, etc.)
  • Time Range Filtering: Relative periods (30d, 6m, 1y, 2y) or absolute date ranges (YYYY-MM-DD to YYYY-MM-DD)
  • Smart Link Extraction: Auto-classifies URLs from abstracts into GitHub, project page, dataset, video, demo, HuggingFace links
  • BibTeX Export: Fetch BibTeX from arXiv official API; export to .bib files with category and date filters
  • LLM Keyword Suggestion: Input paper titles or arXiv IDs to auto-generate optimized search keywords via OpenAI-compatible API
  • Automated Paper Collection: Daily automatic crawling with GitHub Actions
  • Intelligent Classification: Auto-categorize papers into 14+ topics (Acceleration, Dynamic Scenes, SLAM, etc.)

🛠️ Technical Features

  • Robust Error Handling: Multi-layer retry and fallback strategies ensure stable operation
  • GitHub Actions Integration: Automated CI/CD workflows for daily updates
  • Multi-type Link Badges: README entries display PDF, GitHub (with stars), Project, Dataset, Video, Demo, HuggingFace, and Citation badges
  • Detailed Logging: Comprehensive logging for debugging and monitoring
  • Cross-Platform: Support for Windows/Linux/macOS

📚 Data Output

  • Paper JSON files (data/papers_YYYY-MM-DD.json): Full paper metadata with title, authors, abstract, links, keywords, BibTeX
  • BibTeX files (output/*.bib): Ready-to-use bibliography files for LaTeX
  • Auto-generated README: Categorized and formatted paper listings

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt
python main.py init

This wizard walks you through:

  • Setting search keywords (for title, abstract, or both)
  • Selecting arXiv domains (e.g., cs.CV, cs.GR, cs.AI)
  • Configuring time range (relative like 6m/1y, or absolute dates)
  • Setting max results
  • Optionally configuring an OpenAI-compatible API key for keyword suggestion

3. Search Papers

# Search with settings from user_config.json
python main.py search

# Override: fetch 200 papers from the last 6 months, include BibTeX
python main.py search --max-results 200 --recent 6m --bibtex

# Search with absolute date range
python main.py search --date-from 2024-01-01 --date-to 2025-01-01

# Include citation counts from Semantic Scholar
python main.py search --citations

4. Export BibTeX

# Export all papers from the latest data file
python main.py export-bib --output output/references.bib

# Export only "Dynamic Scene" papers
python main.py export-bib --category "Dynamic Scene" --output output/dynamic.bib

# Export papers from a specific date range
python main.py export-bib --date-from 2024-06-01 --date-to 2025-01-01 --output output/recent.bib

5. LLM Keyword Suggestion

# Generate keywords from paper titles
python main.py suggest --titles "3D Gaussian Splatting for Real-Time Rendering" "Dynamic 3D Gaussians"

# Generate from arXiv IDs (auto-fetches titles)
python main.py suggest --arxiv-ids 2308.04079 2311.12897

# Auto-write suggested keywords to config
python main.py suggest --titles "NeRF" "Gaussian Splatting" --apply

# Use a custom API endpoint (e.g., DeepSeek)
python main.py suggest --titles "Paper Title" --base-url https://api.deepseek.com/v1 --api-key sk-xxx --model deepseek-chat

6. Generate README

# Basic README
python main.py readme

# Include latest papers section and abstracts
python main.py readme --show-latest --show-abstracts

Configuration File

All settings are stored in data/user_config.json:

{
  "search": {
    "keywords": {
      "both_abstract_and_title": ["gaussian splatting", "3d gaussian"],
      "abstract_only": ["neural radiance field gaussian"],
      "title_only": ["3D scene reconstruction"]
    },
    "domains": ["cs.CV", "cs.GR"],
    "time_range": {
      "mode": "relative",
      "relative": "1y"
    },
    "max_results": 500
  },
  "api_keys": {
    "openai_api_key": "",
    "openai_base_url": "https://api.openai.com/v1",
    "openai_model": "gpt-4o-mini"
  }
}

Contribution Guidelines

Feel free to submit Pull Requests to improve this list! Please follow these formats:

  • Paper entry format: **[Paper Title](link)** - Brief description
  • Project entry format: [Project Name](link) - Project description

License

CC0

Contribuer sur GitHub

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