Linketic/CityGaussian

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1251 stars · 110 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of the CityGaussian series (ECCV 2024, ICLR 2025) for high-quality large-scale 3D scene reconstruction with Gaussian Splatting. It provides multi-GPU training, mesh extraction, trajectory-aligned rendering, and joint pose optimization built on Gaussian Lightning.

Use cases

• reconstruct large-scale city scenes from multi-view images with gaussian splatting
• extract meshes from 3dgs reconstructions of large scenes
• render novel view videos along camera trajectories
• train 3dgs across multiple gpus with bounded memory
• refine imperfect COLMAP poses jointly with 3dgs optimization
• evaluate geometric accuracy of large-scale scene reconstructions

When to choose

• you need real-time high-quality rendering of city-scale or other large scenes
• you want geometrically accurate surfaces and mesh extraction from gaussian splatting
• you need multi-GPU reconstruction with controllable memory cost
• you want a research-grade 3DGS pipeline with pose refinement and web viewer

When to avoid

• you need a permissively licensed library for commercial use (CC BY-NC-SA 4.0)
• you only need small-object or single-room 3DGS reconstruction without large-scale partitioning
• you need a turnkey photogrammetry tool rather than a research codebase
• you lack GPU resources, as training is GPU-intensive

Facets

library · maturity active

computer-vision graphics machine-learning image-processing computer-vision graphics artificial-intelligence deep-learning python gaussian-splatting 3dgs novel-view-synthesis radiance-field mesh-extraction large-scale-scenes research-code eccv2024 iclr2025 multi-gpu-training gpu linux cuda

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Linketic/CityGaussian")